Continual Learning Papers

Continual Learning papers list, curated by ContinualAI. Search among 343 papers!

You can browse the list in this file or interactively on the ContinualAI website.

Join our community on Slack to stay updated with the latest Continual Learning news.
Visit the Continua AI wiki → http://wiki.continualai.org/

Table of contents

Add a new paper
Join the ContinualAI Zotero group

• List of papers
  • Applications
  • Architectural methods
  • Benchmarks
  • Bio-inspired methods
  • Catastrophic Forgetting studies
  • Classics
  • Continual Few-shot learning
  • Continual Meta Learning
  • Continual Reinforcement Learning
  • Continual Sequential Learning
  • Dissertation and theses
  • Generative Replay methods
  • Hybrid methods
  • Meta Continual Learning
  • Metrics and Evaluation
  • Neuroscience
  • Others
  • Regularization methods
  • Rehearsal methods
  • Review papers and books
  • Robotics

---

Add a new paper

The list of papers is maintained through a Zotero group. You can join the group and help us keeping it updated (see next section).

If you don't want to join the group, you can simply open a Github issue to suggest us a new paper (or even more than one). We will take care of adding it to the list as soon as possible.

1. Open a new Github issue.

2. Attach your bib file containing the paper you want to include in the list. If you don't have a bib file, just provide us with the link to the paper. The link should point to a location where paper metadata can be appropriately retrieved by common reference managers.

Alternatively, you can submit a Pull Request with a modification to the bibtex files directly!

Join the ContinualAI Zotero group

You can give your contribution to the group by adding new papers or by helping annotating the existing ones.

1. Join our Zotero group

2. To add a new paper

   2.1. Add it to the group folder which best represents the paper contribution. Read some advices below if you are uncertain on this. You can add the paper from your library or directly from the paper webpage through the Zotero web browser plugin.

   2.2 Make sure that at least title, authors, item type and publication are specified. The year must be put inside date field.

   2.3 Also put a link to the paper in the url field.

3. To annotate an existing paper

   3.1. Check the list of existing tags in tags.csv file. If you want to add a new tag, please add it in there and submit a Pull Request.

   3.2. Add your tags in the Tags tab of Zotero. Please, remember to write the tag in square brackets e.g. [mytag]

   3.3. Add your notes in the Notes tab of Zotero.

We will periodically export the bibtex to keep the list updated. In case we forgot, join the ContinualAI Slack and complain about our behavior in the #wiki channel.

Advices to add new papers in Zotero

• Check if the paper already exist by using the Citation Key or the title in Zotero search bar.

• Don't forget to add the publication venue (Journal, Proceedings...). Use publication = arXiv if the paper is a preprint.

• We use a system based on categories. This can sometimes be limiting. In general, please consider to add the paper in the category which you consider the most relevant one. You can add the paper in at most 2 categories, if you believe that both are equally relevant.

• Please, do not add new tags if a similar category already exists.

---

List of papers

Applications

26 papers

In this section we maintain a list of all applicative papers produced on continual learning and related topics.

• Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data by Hamed Hemati, Marco Schreyer and Damian Borth. AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance, 2022.
• The Traffic Flow Prediction Method Using the Incremental Learning-Based CNN-LTSM Model: The Solution of Mobile Application by Yanli Shao, Yiming Zhao, Feng Yu, Huawei Zhu and Jinglong Fang. Mobile Information Systems, e5579451, 2021. [experimental]
• Findings of the First Shared Task on Lifelong Learning Machine Translation by Loïc Barrault, Magdalena Biesialska, Marta R. Costa-jussà, Fethi Bougares and Olivier Galibert. Proceedings of the Fifth Conference on Machine Translation, 56--64, 2020. [framework] [nlp]
• Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders by Damian Campo, Giulia Slavic, Mohamad Baydoun, Lucio Marcenaro and Carlo Regazzoni. arXiv, 2020. [vision]
• Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem by Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh and Tinne Tuytelaars. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14451--14460, 2020. [framework] [mnist] [vision]
• Incremental Learning for End-to-End Automatic Speech Recognition by Li Fu, Xiaoxiao Li and Libo Zi. arXiv, 2020. [audio]
• Neural Topic Modeling with Continual Lifelong Learning by Pankaj Gupta, Yatin Chaudhary, Thomas Runkler and Hinrich Schütze. ICML, 2020. [nlp]
• CLOPS: Continual Learning of Physiological Signals by Dani Kiyasseh, Tingting Zhu and David A Clifton. arXiv, 2020.
• Clinical Applications of Continual Learning Machine Learning by Cecilia S Lee and Aaron Y Lee. The Lancet Digital Health, e279--e281, 2020.
• Continual Learning for Domain Adaptation in Chest X-ray Classification by Matthias Lenga, Heinrich Schulz and Axel Saalbach. arXiv, 1--11, 2020. [vision]
• Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis by Avinash Madasu and Vijjini Anvesh Rao. arXiv, 2020. [nlp] [rnn]
• RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning by Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew Bagdanov and Joost van de Weijer. Advances in Neural Information Processing Systems, 16736--16748, 2020. [nlp]
• Importance Driven Continual Learning for Segmentation Across Domains by Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy and Christian Wachinger. arXiv, 1--10, 2020. [vision]
• LAMOL: LAnguage MOdeling for Lifelong Language Learning by Fan-Keng Sun, Cheng-Hao Ho and Hung-Yi Lee. ICLR, 2020. [nlp]
• Non-Parametric Adaptation for Neural Machine Translation by Ankur Bapna and Orhan Firat. arXiv, 2019. [nlp]
• Episodic Memory in Lifelong Language Learning by Cyprien de Masson D'Autume, Sebastian Ruder, Lingpeng Kong and Dani Yogatama. NeurIPS, 2019. [nlp]
• Continual Adaptation for Efficient Machine Communication by Robert D Hawkins, Minae Kwon, Dorsa Sadigh and Noah D Goodman. Proceedings of the ICML Workshop on Adaptive & Multitask Learning: Algorithms & Systems, 2019.
• Continual Learning for Sentence Representations Using Conceptors by Tianlin Liu, Lyle Ungar and João Sedoc. NAACL, 2019. [nlp]
• [Lifelong and Interactive Learning of Factual Knowledge in Dialogues](http://arxiv.org/abs/1907.13295 https://www.aclweb.org/anthology/W19-5903) by Sahisnu Mazumder, Bing Liu, Shuai Wang and Nianzu Ma. Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, 21--31, 2019. [nlp]
• Making Good on LSTMs' Unfulfilled Promise by Daniel Philps, Artur d'Avila Garcez and Tillman Weyde. arXiv, 2019. [rnn]
• Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation by Brian Thompson, Jeremy Gwinnup, Huda Khayrallah, Kevin Duh and Philipp Koehn. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2062--2068, 2019. [nlp] [rnn]
• Lifelong Learning for Scene Recognition in Remote Sensing Images by Min Zhai, Huaping Liu and Fuchun Sun. IEEE Geoscience and Remote Sensing Letters, 1472--1476, 2019. [vision]
• Towards Continual Learning in Medical Imaging by Chaitanya Baweja, Ben Glocker and Konstantinos Kamnitsas. NeurIPS Workshop on Continual Learning, 1--4, 2018. [vision]
• Toward Continual Learning for Conversational Agents by and Sungjin Lee. arXiv, 2018. [nlp]
• Toward an Architecture for Never-Ending Language Learning by Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka and Tom M. Mitchell. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 1306--1313, 2010. [nlp]
• Principles of Lifelong Learning for Predictive User Modeling by Ashish Kapoor and Eric Horvitz. User Modeling 2007, 37--46, 2009.

Architectural Methods

36 papers

In this section we collect all the papers introducing a continual learning strategy employing some architectural methods.

• Provable and Efficient Continual Representation Learning by Yingcong Li, Mingchen Li, M. Salman Asif and Samet Oymak. arXiv, 2022.
• Architecture Matters in Continual Learning by Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Gorur and Mehrdad Farajtabar. arXiv, 2022.
• The Multiple Subnetwork Hypothesis: Enabling Multidomain Learning by Isolating Task-Specific Subnetworks in Feedforward Neural Networks by Jacob Renn, Ian Sotnek, Benjamin Harvey and Brian Caffo. , 2022. [sparsity]
• Continual Learning with Node-Importance Based Adaptive Group Sparse Regularization by Sangwon Jung, Hongjoon Ahn, Sungmin Cha and Taesup Moon. , 2021.
• Structured Ensembles: An Approach to Reduce the Memory Footprint of Ensemble Methods by Jary Pomponi, Simone Scardapane and Aurelio Uncini. Neural Networks, 407--418, 2021.
• Continual Learning via Bit-Level Information Preserving by Yujun Shi, Li Yuan, Yunpeng Chen and Jiashi Feng. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16674--16683, 2021.
• SpaceNet: Make Free Space for Continual Learning by Ghada Sokar, Decebal Constantin Mocanu and Mykola Pechenizkiy. Neurocomputing, 1--11, 2021. [cifar] [fashion] [mnist] [sparsity]
• Modular Dynamic Neural Network: A Continual Learning Architecture by Daniel Turner, Pedro J. S. Cardoso and João M. F. Rodrigues. Applied Sciences, 12078, 2021.
• Continual Learning with Adaptive Weights (CLAW) by Tameem Adel, Han Zhao and Richard E Turner. International Conference on Learning Representations, 2020. [cifar] [mnist] [omniglot]
• Continual Learning with Gated Incremental Memories for Sequential Data Processing by Andrea Cossu, Antonio Carta and Davide Bacciu. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020. [mnist] [rnn]
• Continual Learning in Recurrent Neural Networks by Benjamin Ehret, Christian Henning, Maria Cervera, Alexander Meulemans, Johannes Von Oswald and Benjamin F. Grewe. International Conference on Learning Representations, 2020. [audio] [rnn]
• Explainability in Deep Reinforcement Learning by Alexandre Heuillet, Fabien Couthouis and Natalia Díaz-Rodr\ǵuez. arXiv:2008.06693 [cs], 2020.
• A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning by Soochan Lee, Junsoo Ha, Dongsu Zhang and Gunhee Kim. International Conference on Learning Representations, 2020.
• Bayesian Nonparametric Weight Factorization for Continual Learning by Nikhil Mehta, Kevin J Liang and Lawrence Carin. arXiv, 1--17, 2020. [bayes] [cifar] [mnist] [sparsity]
• Efficient Continual Learning with Modular Networks and Task-Driven Priors by Tom Veniat, Ludovic Denoyer and Marc'Aurelio Ranzato. arXiv, 2020. [experimental]
• Progressive Memory Banks for Incremental Domain Adaptation by Nabiha Asghar, Lili Mou, Kira A Selby, Kevin D Pantasdo, Pascal Poupart and Xin Jiang. International Conference on Learning Representations, 2019. [nlp] [rnn]
• Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments by Andri Ashfahani and Mahardhika Pratama. Proceedings of the 2019 SIAM International Conference on Data Mining, 666--674, 2019. [mnist]
• Compacting, Picking and Growing for Unforgetting Continual Learning by Steven C Y Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan and Chu-Song Chen. NeurIPS, 13669--13679, 2019. [cifar] [imagenet]
• Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting by Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher and Caiming Xiong. arXiv, 2019. [cifar] [mnist]
• Towards AutoML in the Presence of Drift: First Results by Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon and Michele Sebag. arXiv, 2019.
• Continual Unsupervised Representation Learning by Dushyant Rao, Francesco Visin, Andrei A Rusu, Yee Whye Teh, Razvan Pascanu and Raia Hadsell. NeurIPS, 2019. [mnist] [omniglot]
• A Progressive Model to Enable Continual Learning for Semantic Slot Filling by Yilin Shen, Xiangyu Zeng and Hongxia Jin. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 1279--1284, 2019. [nlp]
• Adaptive Compression-based Lifelong Learning by Shivangi Srivastava, Maxim Berman, Matthew B Blaschko and Devis Tuia. BMVC, 2019. [imagenet] [sparsity]
• Frosting Weights for Better Continual Training by Xiaofeng Zhu, Feng Liu, Goce Trajcevski and Dingding Wang. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 506--510, 2019. [cifar] [mnist]
• Dynamic Few-Shot Visual Learning Without Forgetting by Spyros Gidaris and Nikos Komodakis. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4367--4375, 2018. [imagenet] [vision]
• HOUDINI: Lifelong Learning as Program Synthesis by Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton and Swarat Chaudhuri. NeurIPS, 8687--8698, 2018.
• Reinforced Continual Learning by Ju Xu and Zhanxing Zhu. Advances in Neural Information Processing Systems, 899--908, 2018. [cifar] [mnist]
• Lifelong Learning With Dynamically Expandable Networks by Jaehong Yoon, Eunho Yang, Jeongtae Lee and Sung Ju Hwang. ICLR, 11, 2018. [cifar] [mnist] [sparsity]
• Expert Gate: Lifelong Learning with a Network of Experts by Rahaf Aljundi, Punarjay Chakravarty and Tinne Tuytelaars. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [vision]
• Neurogenesis Deep Learning by Timothy John Draelos, Nadine E Miner, Christopher Lamb, Jonathan A Cox, Craig Michael Vineyard, Kristofor David Carlson, William Mark Severa, Conrad D James and James Bradley Aimone. IJCNN, 2017. [mnist]
• Net2Net: Accelerating Learning via Knowledge Transfer by Tianqi Chen, Ian Goodfellow and Jonathon Shlens. ICLR, 2016.
• Continual Learning through Evolvable Neural Turing Machines by Benno Luders, Mikkel Schlager and Sebastian Risi. NIPS 2016 Workshop on Continual Learning and Deep Networks, 2016.
• Progressive Neural Networks by Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu and Raia Hadsell. arXiv, 2016. [mnist]
• Knowledge Transfer in Deep Block-Modular Neural Networks by Alexander V. Terekhov, Guglielmo Montone and J. Kevin O'Regan. Conference on Biomimetic and Biohybrid Systems, 268--279, 2015. [vision]
• ELLA: An Efficient Lifelong Learning Algorithm by Paul Ruvolo and Eric Eaton. International Conference on Machine Learning, 507--515, 2013.
• A Self-Organising Network That Grows When Required by Stephen Marsland, Jonathan Shapiro and Ulrich Nehmzow. Neural Networks, 1041--1058, 2002. [som]

Benchmarks

12 papers

In this section we list all the papers related to new benchmarks proposals for continual learning and related topics.

• vCLIMB: A Novel Video Class Incremental Learning Benchmark by Andrés Villa, Kumail Alhamoud, Juan León Alcázar, Fabian Caba Heilbron, Victor Escorcia and Bernard Ghanem. arXiv, 2022.
• Is Class-Incremental Enough for Continual Learning? by Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, Davide Maltoni, Davide Bacciu, Antonio Carta and Vincenzo Lomonaco. arXiv, 2021.
• A Procedural World Generation Framework for Systematic Evaluation of Continual Learning by Timm Hess, Martin Mundt, Iuliia Pliushch and Visvanathan Ramesh. Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021.
• Efficient Continual Learning with Modular Networks and Task-Driven Priors by Tom Veniat, Ludovic Denoyer and Marc'Aurelio Ranzato. ICLR, 2021.
• Defining Benchmarks for Continual Few-Shot Learning by Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal and Amos Storkey. arXiv, 2020. [imagenet]
• Evaluating Online Continual Learning with CALM by Germán Kruszewski, Ionut-Teodor Sorodoc and Tomas Mikolov. arXiv, 2020. [nlp] [rnn]
• Continual Reinforcement Learning in 3D Non-Stationary Environments by Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello and Davide Maltoni. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 248--249, 2020.
• Stream-51: Streaming Classification and Novelty Detection From Videos by Ryne Roady, Tyler L. Hayes, Hitesh Vaidya and Christopher Kanan. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 228--229, 2020.
• OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning by Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, Fei Qiao and Rosa H M Chan. arXiv, 1--8, 2019. [vision]
• Incremental Object Learning From Contiguous Views by Stefan Stojanov, Samarth Mishra, Ngoc Anh Thai, Nikhil Dhanda, Ahmad Humayun, Chen Yu, Linda B. Smith and James M. Rehg. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8777--8786, 2019.
• New Metrics and Experimental Paradigms for Continual Learning by Tyler L. Hayes, Ronald Kemker, Nathan D. Cahill and Christopher Kanan. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2112--21123, 2018.
• CORe50: A New Dataset and Benchmark for Continuous Object Recognition by Vincenzo Lomonaco and Davide Maltoni. Proceedings of the 1st Annual Conference on Robot Learning, 17--26, 2017. [vision]

Bioinspired Methods

24 papers

In this section we list all the papers related to bioinspired continual learning approaches.

• A Biologically Plausible Audio-Visual Integration Model for Continual Learning by Wenjie Chen, Fengtong Du, Ye Wang and Lihong Cao. IJCNN, 2021.
• Synaptic Metaplasticity in Binarized Neural Networks by Axel Laborieux, Maxence Ernoult, Tifenn Hirtzlin and Damien Querlioz. Nature Communications, 2549, 2021.
• Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks by Jason M. Allred and Kaushik Roy. Frontiers in Neuroscience, 7, 2020. [spiking]
• [Storing Encoded Episodes as Concepts for Continual Learning](https://arxiv.org/abs/2007.06637 http://arxiv.org/abs/2007.06637) by Ali Ayub and Alan R. Wagner. arXiv, 2020. [generative] [imagenet] [mnist]
• Cognitively-Inspired Model for Incremental Learning Using a Few Examples by A. Ayub and A. R. Wagner. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. [cifar] [cubs] [dual]
• Spiking Neural Predictive Coding for Continual Learning from Data Streams by and Alexander Ororbia. arXiv, 2020. [spiking]
• Brain-like Replay for Continual Learning with Artificial Neural Networks by Gido M. van de Ven, Hava T. Siegelmann and Andreas S. Tolias. International Conference on Learning Representations (Workshop on Bridging AI and Cognitive Science), 2020. [cifar]
• Selfless Sequential Learning by Rahaf Aljundi, Marcus Rohrbach and Tinne Tuytelaars. ICLR, 2019. [cifar] [mnist] [sparsity]
• Backpropamine: Training Self-Modifying Neural Networks with Differentiable Neuromodulated Plasticity by Thomas Miconi, Aditya Rawal, Jeff Clune and Kenneth O Stanley. ICLR, 2019.
• Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations by Alexander Ororbia, Ankur Mali, C Lee Giles and Daniel Kifer. arXiv, 2019. [mnist] [rnn] [spiking]
• Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting When Learning Cumulatively by Alexander Ororbia, Ankur Mali, Daniel Kifer and C Lee Giles. arXiv, 1--11, 2019. [fashion] [mnist] [sparsity]
• FearNet: Brain-Inspired Model for Incremental Learning by Ronald Kemker and Christopher Kanan. ICLR, 2018. [audio] [cifar] [generative]
• Differentiable Plasticity: Training Plastic Neural Networks with Backpropagation by Thomas Miconi, Kenneth Stanley and Jeff Clune. International Conference on Machine Learning, 3559--3568, 2018.
• Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization by German I Parisi, Jun Tani, Cornelius Weber and Stefan Wermter. Frontiers in Neurorobotics, 2018. [core50] [dual] [rnn] [som]
• SLAYER: Spike Layer Error Reassignment in Time by Sumit Bam Shrestha and Garrick Orchard. Advances in Neural Information Processing Systems 31, 1412--1421, 2018.
• Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World by Sahil Garg, Irina Rish, Guillermo Cecchi and Aurelie Lozano. IJCAI International Joint Conference on Artificial Intelligence, 1696--1702, 2017. [nlp] [vision]
• [Diffusion-Based Neuromodulation Can Eliminate Catastrophic Forgetting in Simple Neural Networks](http://arxiv.org/abs/1705.07241 http://dx.doi.org/10.1371/journal.pone.0187736) by Roby Velez and Jeff Clune. PLoS ONE, 1--31, 2017.
• How Do Neurons Operate on Sparse Distributed Representations? A Mathematical Theory of Sparsity, Neurons and Active Dendrites by Subutai Ahmad and Jeff Hawkins. arXiv, 1--23, 2016. [hebbian] [sparsity]
• Continuous Online Sequence Learning with an Unsupervised Neural Network Model by Yuwei Cui, Subutai Ahmad and Jeff Hawkins. Neural Computation, 2474--2504, 2016. [spiking]
• Backpropagation of Hebbian Plasticity for Continual Learning by and Thomas Miconi. NIPS Workshop - Continual Learning, 5, 2016.
• Mitigation of Catastrophic Forgetting in Recurrent Neural Networks Using a Fixed Expansion Layer by Robert Coop and Itamar Arel. The 2013 International Joint Conference on Neural Networks (IJCNN), 1--7, 2013. [mnist] [rnn] [sparsity]
• Compete to Compute by Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez and Jürgen Schmidhuber. Advances in Neural Information Processing Systems 26, 2013. [mnist] [sparsity]
• Mitigation of Catastrophic Interference in Neural Networks Using a Fixed Expansion Layer by Robert Coop and Itamar Arel. 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), 726--729, 2012. [sparsity]
• Synaptic Plasticity: Taming the Beast by L F Abbott and Sacha B Nelson. Nature Neuroscience, 1178--1183, 2000. [hebbian]

Catastrophic Forgetting Studies

18 papers

In this section we list all the major contributions trying to understand catastrophic forgetting and its implication in machines that learn continually.

• Architecture Matters in Continual Learning by Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Gorur and Mehrdad Farajtabar. arXiv, 2022.
• Continual Learning in the Teacher-Student Setup: Impact of Task Similarity by Sebastian Lee, Sebastian Goldt and Andrew Saxe. International Conference on Machine Learning, 6109--6119, 2021.
• Continual Learning in Deep Networks: An Analysis of the Last Layer by Timothée Lesort, Thomas George and Irina Rish. arXiv, 2021.
• Understanding Continual Learning Settings with Data Distribution Drift Analysis by Timothée Lesort, Massimo Caccia and Irina Rish. arXiv, 2021.
• Wide Neural Networks Forget Less Catastrophically by Seyed Iman Mirzadeh, Arslan Chaudhry, Huiyi Hu, Razvan Pascanu, Dilan Gorur and Mehrdad Farajtabar. arXiv, 2021.
• Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics by Vinay Venkatesh Ramasesh, Ethan Dyer and Maithra Raghu. International Conference on Learning Representations, 2021.
• Does Continual Learning = Catastrophic Forgetting? by Anh Thai, Stefan Stojanov, Isaac Rehg and James M. Rehg. arXiv, 2021.
• Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget by Guy Davidson and Michael C Mozer. CVPR, 9282--9293, 2020. [vision]
• Understanding the Role of Training Regimes in Continual Learning by Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu and Hassan Ghasemzadeh. arXiv, 2020.
• Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization by Giang Nguyen, Shuan Chen, Thao Do, Tae Joon Jun, Ho-Jin Choi and Daeyoung Kim. arXiv, 2020. [vision]
• Toward Understanding Catastrophic Forgetting in Continual Learning by Cuong V Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan and Stefano Soatto. arXiv, 2019. [cifar] [mnist]
• A Study on Catastrophic Forgetting in Deep LSTM Networks by Monika Schak and Alexander Gepperth. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning, 714--728, 2019. [rnn]
• An Empirical Study of Example Forgetting during Deep Neural Network Learning by Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio and Geoffrey J Gordon. International Conference on Learning Representations, 2019. [cifar] [mnist]
• Localizing Catastrophic Forgetting in Neural Networks by Felix Wiewel and Bin Yang. arXiv, 2019. [mnist]
• An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks by Ian J. Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville and Yoshua Bengio. arXiv, 2015.
• [The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects](http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3732997%7B%5C&%7Dtool=pmcentrez%7B%5C&%7Drendertype=abstract http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00504/abstract) by Martial Mermillod, Aurélia Bugaiska and Patrick Bonin. Frontiers in Psychology, 504, 2013.
• Catastrophic Forgetting in Connectionist Networks by and Robert French. Trends in Cognitive Sciences, 128--135, 1999. [sparsity]
• How Does a Brain Build a Cognitive Code? by and Stephen Grossberg. Psychological Review, 1--51, 1980.

Classics

10 papers

In this section you'll find pioneering and classic continual learning papers. We recommend to read all the papers in this section for a good background on current continual deep learning developments.

• Lifelong Machine Learning: A Paradigm for Continuous Learning by and Bing Liu. Frontiers of Computer Science, 359--361, 2017.
• [The Organization of Behavior: A Neuropsychological Theory](https://www.amazon.com/Organization-Behavior-Neuropsychological-Theory/dp/0805843000 https://books.google.it/books/about/The_Organization_of_Behavior.html?id=ddB4AgAAQBAJ&printsec=frontcover&source=kp_read_button&redir_esc=y#v=onepage&q&f=false) by and D O Hebb. Lawrence Erlbaum, 2002. [hebbian]
• Pseudo-Recurrent Connectionist Networks: An Approach to the 'Sensitivity-Stability' Dilemma by and Robert French. Connection Science, 353--380, 1997. [dual]
• CHILD: A First Step Towards Continual Learning by and Mark B Ring. Machine Learning, 77--104, 1997.
• Is Learning The N-Th Thing Any Easier Than Learning The First? by and Sebastian Thrun. Advances in Neural Information Processing Systems 8, 640--646, 1996. [vision]
• [Learning in the Presence of Concept Drift and Hidden Contexts](https://doi.org/10.1007/BF00116900 http://link.springer.com/10.1007/BF00116900) by Gerhard Widmer and Miroslav Kubat. Machine Learning, 69--101, 1996.
• Using Semi-Distributed Representations to Overcome Catastrophic Forgetting in Connectionist Networks by and Robert French. In Proceedings of the 13th Annual Cognitive Science Society Conference, 173--178, 1991. [sparsity]
• Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions by and R. Ratcliff. Psychological Review, 285--308, 1990.
• The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network by Gail A. Carpenter and Stephen Grossberg. Computer, 77--88, 1988.
• How Does a Brain Build a Cognitive Code? by and Stephen Grossberg. Psychological Review, 1--51, 1980.

Continual Few Shot Learning

8 papers

Here we list the papers related to Few-Shot continual and incremental learning.

• Few-Shot Continual Learning: A Brain-Inspired Approach by Liyuan Wang, Qian Li, Yi Zhong and Jun Zhu. arXiv, 2021.
• Defining Benchmarks for Continual Few-Shot Learning by Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal and Amos Storkey. arXiv, 2020. [imagenet]
• Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot by Ali Ayub and Alan R. Wagner. arXiv, 2020.
• La-MAML: Look-ahead Meta Learning for Continual Learning by Gunshi Gupta, Karmesh Yadav and Liam Paull. arXiv, 2020.
• iTAML: An Incremental Task-Agnostic Meta-learning Approach by Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan and Mubarak Shah. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13588---13597, 2020. [cifar] [imagenet]
• Wandering within a World: Online Contextualized Few-Shot Learning by Mengye Ren, Michael L Iuzzolino, Michael C Mozer and Richard S Zemel. arXiv, 2020. [omniglot]
• Few-Shot Class-Incremental Learning by X. Tao, Hong X., X. Chang, S. Dong, X. Wei and Y. Gong. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [cifar]
• Few-Shot Class-Incremental Learning via Feature Space Composition by H. Zhao, Y. Fu, X. Li, S. Li, B. Omar and X. Li. arXiv, 2020. [cifar] [cubs]

Continual Meta Learning

4 papers

In this section we list all the papers related to the continual meta-learning.

• Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning by Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexande Lacoste, David Vazquez and Laurent Charlin. arXiv, 2020. [fashion] [framework] [mnist]
• Continuous Meta-Learning without Tasks by James Harrison, Apoorva Sharma, Chelsea Finn and Marco Pavone. arXiv, 2019. [imagenet] [mnist]
• Task Agnostic Continual Learning via Meta Learning by Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A Rusu, Yee Whye Teh and Razvan Pascanu. arXiv:1906.05201 [cs, stat], 2019. [mnist]
• Reconciling Meta-Learning and Continual Learning with Online Mixtures of Tasks by Ghassen Jerfel, Erin Grant, Tom Griffiths and Katherine A Heller. Advances in Neural Information Processing Systems, 9122--9133, 2019. [bayes] [vision]

Continual Reinforcement Learning

23 papers

In this section we list all the papers related to the continual Reinforcement Learning.

• Lifetime Policy Reuse and the Importance of Task Capacity by David M. Bossens and Adam J. Sobey. arXiv, 2021.
• Unsupervised Lifelong Learning with Curricula by Yi He, Sheng Chen, Baijun Wu, Xu Yuan and Xindong Wu. Proceedings of the Web Conference 2021, 3534--3545, 2021.
• Continuous Coordination As a Realistic Scenario for Lifelong Learning by Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville and Sarath Chandar. International Conference on Machine Learning, 8016--8024, 2021.
• Reducing Catastrophic Forgetting When Evolving Neural Networks by and Joseph Early. arXiv, 2019.
• A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning by Francisco M Garcia and Philip S Thomas. NeurIPS, 5691--5700, 2019.
• Policy Consolidation for Continual Reinforcement Learning by Christos Kaplanis, Murray Shanahan and Claudia Clopath. ICML, 2019.
• Continual Learning Exploiting Structure of Fractal Reservoir Computing by Taisuke Kobayashi and Toshiki Sugino. Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 35--47, 2019. [rnn]
• Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL by Anusha Nagabandi, Chelsea Finn and Sergey Levine. 7th International Conference on Learning Representations, ICLR 2019, 2019.
• Leaky Tiling Activations: A Simple Approach to Learning Sparse Representations Online by Yangchen Pan, Kirby Banman and Martha White. arXiv, 2019. [sparsity]
• Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference by Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu and Gerald Tesauro. ICLR, 2019. [mnist]
• Experience Replay for Continual Learning by David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P Lillicrap and Greg Wayne. NeurIPS, 350--360, 2019.
• Selective Experience Replay for Lifelong Learning by David Isele and Akansel Cosgun. Thirty-Second AAAI Conference on Artificial Intelligence, 3302--3309, 2018.
• Continual Reinforcement Learning with Complex Synapses by Christos Kaplanis, Murray Shanahan and Claudia Clopath. ICML, 2018.
• Unicorn: Continual Learning with a Universal, Off-policy Agent by Daniel J Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver and Tom Schaul. arXiv, 1--17, 2018.
• Lifelong Inverse Reinforcement Learning by Jorge A Mendez, Shashank Shivkumar and Eric Eaton. NeurIPS, 4502--4513, 2018.
• Progress & Compress: A Scalable Framework for Continual Learning by Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu and Raia Hadsell. International Conference on Machine Learning, 4528--4537, 2018. [vision]
• Overcoming Catastrophic Forgetting in Neural Networks by James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran and Raia Hadsell. PNAS, 3521--3526, 2017. [mnist]
• Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory by Benno Lüders, Mikkel Schläger, Aleksandra Korach and Sebastian Risi. Applications of Evolutionary Computation, 886--901, 2017.
• [Stable Predictive Representations with General Value Functions for Continual Learning](https://sites.ualberta.ca/ amw8/cldl.pdf) by Matthew Schlegel, Adam White and Martha White. Continual Learning and Deep Networks Workshop at the Neural Information Processing System Conference, 2017.
• Continual Learning through Evolvable Neural Turing Machines by Benno Luders, Mikkel Schlager and Sebastian Risi. NIPS 2016 Workshop on Continual Learning and Deep Networks, 2016.
• Progressive Neural Networks by Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu and Raia Hadsell. arXiv, 2016. [mnist]
• [Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets.](http://www.ncbi.nlm.nih.gov/pubmed/29756130 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5947972) by Lei Shu, Bing Liu, Hu Xu and Annice Kim. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 225--235, 2016. [nlp]
• CHILD: A First Step Towards Continual Learning by and Mark B Ring. Machine Learning, 77--104, 1997.

Continual Sequential Learning

13 papers

Here we maintain a list of all the papers related to the continual learning at the intersection with sequential learning.

• Continual Sequence Generation with Adaptive Compositional Modules by Yanzhe Zhang, Xuezhi Wang and Diyi Yang. ACL, 2022. [nlp]
• Continual Learning for Recurrent Neural Networks: An Empirical Evaluation by Andrea Cossu, Antonio Carta, Vincenzo Lomonaco and Davide Bacciu. Neural Networks, 607--627, 2021. [rnn]
• Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning by and Alexander G. Ororbia. arXiv, 2021.
• Continual Learning with Gated Incremental Memories for Sequential Data Processing by Andrea Cossu, Antonio Carta and Davide Bacciu. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020. [mnist] [rnn]
• Organizing Recurrent Network Dynamics by Task-Computation to Enable Continual Learning by Lea Duncker, Laura N Driscoll, Krishna V Shenoy, Maneesh Sahani and David Sussillo. Advances in Neural Information Processing Systems, 2020. [rnn]
• Meta-Consolidation for Continual Learning by K J Joseph and Vineeth N Balasubramanian. NeurIPS, 2020. [bayes] [cifar] [imagenet] [mnist]
• Compositional Language Continual Learning by Yuanpeng Li, Liang Zhao, Kenneth Church and Mohamed Elhoseiny. Eighth International Conference on Learning Representations, 2020. [nlp] [rnn]
• Online Continual Learning on Sequences by German I Parisi and Vincenzo Lomonaco. Studies in Computational Intelligence, 2020. [framework]
• Unsupervised Progressive Learning and the STAM Architecture by James Smith, Seth Baer, Cameron Taylor and Constantine Dovrolis. arXiv, 2019. [mnist]
• Toward Training Recurrent Neural Networks for Lifelong Learning by Shagun Sodhani, Sarath Chandar and Yoshua Bengio. Neural Computation, 1--35, 2019. [rnn]
• Semi-Supervised Tuning from Temporal Coherence by Davide Maltoni and Vincenzo Lomonaco. 2016 23rd International Conference on Pattern Recognition (ICPR), 2509--2514, 2016.
• Self-Refreshing Memory in Artificial Neural Networks: Learning Temporal Sequences without Catastrophic Forgetting by Bernard Ans, Stéphane Rousset, Robert M. French and Serban Musca. Connection Science, 71--99, 2004. [rnn]
• Using Pseudo-Recurrent Connectionist Networks to Solve the Problem of Sequential Learning by and Robert French. Proceedings of the 19th Annual Cognitive Science Society Conference, 1997. [dual]

Dissertation and Theses

10 papers

In this section we maintain a list of all the dissertation and thesis produced on continual learning and related topics.

• Knowledge Uncertainty and Lifelong Learning in Neural Systems by and Christian Henning. , 2022.
• An Introduction to Lifelong Supervised Learning by Shagun Sodhani, Mojtaba Faramarzi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Janarthanan and Sarath Chandar. , 2022.
• Large-Scale Deep Class-Incremental Learning. (Apprentissage Incrémental Profond à Large ̧́helle) by and Eden Belouadah. , 2021.
• Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes by and Timoth'ee Lesort. arXiv, 2020. [cifar] [framework] [generative] [mnist] [vision]
• Open Set Classification for Deep Learning in Large-Scale and Continual Learning Models by and Ryne Roady. Theses, 2020.
• Continual Learning in Neural Networks by and Rahaf Aljundi. arXiv, 2019. [cifar] [imagenet] [mnist] [vision]
• Continual Deep Learning via Progressive Learning by and Haytham M. Fayek. RMIT University, 2019. [audio] [cifar] [imagenet] [sparsity]
• Continual Learning with Deep Architectures by and Vincenzo Lomonaco. University of Bologna, 2019. [core50] [framework]
• Explanation-Based Neural Network Learning: A Lifelong Learning Approach by and Sebastian Thrun. Springer, 1996. [framework]
• [Continual Learning in Reinforcement Environments](https://www.cs.utexas.edu/ ring/Ring-dissertation.pdf) by and Mark Ring. University of Texas, 1994. [framework]

Generative Replay Methods

8 papers

In this section we collect all the papers introducing a continual learning strategy employing some generative replay methods.

• Foundational Models for Continual Learning: An Empirical Study of Latent Replay by Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish and Laurent Charlin. arXiv, 2022.
• Brain-Inspired Replay for Continual Learning with Artificial Neural Networks by Gido M. van de Ven, Hava T. Siegelmann and Andreas S. Tolias. Nature Communications, 2020. [cifar] [framework] [generative] [mnist]
• Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay by Mohammad Rostami, Soheil Kolouri and Praveen K Pilly. arXiv, 2019.
• Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay by Mohammad Rostami, Soheil Kolouri and Praveen K. Pilly. arXiv, 2019.
• Continual Learning of New Sound Classes Using Generative Replay by Zhepei Wang, Cem Subakan, Efthymios Tzinis, Paris Smaragdis and Laurent Charlin. arXiv, 2019. [audio]
• Generative Replay with Feedback Connections as a General Strategy for Continual Learning by Gido M. van de Ven and Andreas S. Tolias. arXiv, 2018. [framework] [generative] [mnist]
• Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory by Benno Lüders, Mikkel Schläger, Aleksandra Korach and Sebastian Risi. Applications of Evolutionary Computation, 886--901, 2017.
• Continual Learning with Deep Generative Replay by Hanul Shin, Jung Kwon Lee, Jaehong Kim and Jiwon Kim. Advances in Neural Information Processing Systems 30, 2990--2999, 2017. [mnist]

Hybrid Methods

12 papers

In this section we collect all the papers introducing a continual learning strategy employing some hybrid methods, mixing different strategies.

• Dark Experience for General Continual Learning: A Strong, Simple Baseline by Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati and SIMONE CALDERARA. Advances in Neural Information Processing Systems, 15920--15930, 2020.
• Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches by Vincenzo Lomonaco, Davide Maltoni and Lorenzo Pellegrini. CVPR Workshop on Continual Learning for Computer Vision, 246--247, 2020. [core50]
• Linear Mode Connectivity in Multitask and Continual Learning by Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur, Razvan Pascanu and Hassan Ghasemzadeh. arXiv, 2020. [cifar] [experimental] [mnist]
• Efficient Continual Learning in Neural Networks with Embedding Regularization by Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco and Aurelio Uncini. Neurocomputing, 139--148, 2020.
• Efficient Lifelong Learning with A-GEM by Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach and Mohamed Elhoseiny. ICLR, 2019. [cifar] [mnist]
• Single-Net Continual Learning with Progressive Segmented Training (PST) by Xiaocong Du, Gouranga Charan, Frank Liu and Yu Cao. arXiv, 1629--1636, 2019. [cifar]
• Continuous Learning in Single-Incremental-Task Scenarios by Davide Maltoni and Vincenzo Lomonaco. Neural Networks, 56--73, 2019. [core50] [framework]
• Toward Training Recurrent Neural Networks for Lifelong Learning by Shagun Sodhani, Sarath Chandar and Yoshua Bengio. Neural Computation, 1--35, 2019. [rnn]
• Continual Learning of New Sound Classes Using Generative Replay by Zhepei Wang, Cem Subakan, Efthymios Tzinis, Paris Smaragdis and Laurent Charlin. arXiv, 2019. [audio]
• Lifelong Learning via Progressive Distillation and Retrospection by Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang and Dahua Lin. ECCV, 2018. [imagenet] [vision]
• Progress & Compress: A Scalable Framework for Continual Learning by Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu and Raia Hadsell. International Conference on Machine Learning, 4528--4537, 2018. [vision]
• Gradient Episodic Memory for Continual Learning by David Lopez-Paz and Marc'Aurelio Ranzato. NIPS, 2017. [cifar] [mnist]

Meta Continual Learning

9 papers

In this section we list all the papers related to the meta-continual learning.

• Learning to Continually Learn by Shawn Beaulieu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth O. Stanley, Jeff Clune and Nick Cheney. ECAI, 2020. [vision]
• Continual Learning with Deep Artificial Neurons by Blake Camp, Jaya Krishna Mandivarapu and Rolando Estrada. arXiv, 2020. [experimental]
• Meta-Consolidation for Continual Learning by K J Joseph and Vineeth N Balasubramanian. NeurIPS, 2020. [bayes] [cifar] [imagenet] [mnist]
• Meta Continual Learning via Dynamic Programming by R Krishnan and Prasanna Balaprakash. arXiv, 2020. [omniglot]
• Online Meta-Learning by Chelsea Finn, Aravind Rajeswaran, Sham Kakade and Sergey Levine. ICML, 2019. [experimental] [mnist]
• Task Agnostic Continual Learning via Meta Learning by Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A Rusu, Yee Whye Teh and Razvan Pascanu. arXiv:1906.05201 [cs, stat], 2019. [mnist]
• Meta-Learning Representations for Continual Learning by Khurram Javed and Martha White. NeurIPS, 2019. [omniglot]
• Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference by Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu and Gerald Tesauro. ICLR, 2019. [mnist]
• Meta Continual Learning by Risto Vuorio, Dong-Yeon Cho, Daejoong Kim and Jiwon Kim. arXiv, 2018. [mnist]

Metrics and Evaluations

10 papers

In this section we list all the papers related to the continual learning evalution protocols and metrics.

• Continual Learning in Deep Networks: An Analysis of the Last Layer by Timothée Lesort, Thomas George and Irina Rish. arXiv, 2021.
• Avalanche: An End-to-End Library for Continual Learning by Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido van de Ven, Martin Mundt, Qi She, Keiland Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German I. Parisi, Fabio Cuzzolin, Andreas Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu and Davide Maltoni. CLVision Workshop at CVPR, 2021.
• CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability by Martin Mundt, Steven Lang, Quentin Delfosse and Kristian Kersting. International Conference on Learning Representations, 2021.
• Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning by Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexande Lacoste, David Vazquez and Laurent Charlin. arXiv, 2020. [fashion] [framework] [mnist]
• Optimal Continual Learning Has Perfect Memory and Is NP-HARD by Jeremias Knoblauch, Hisham Husain and Tom Diethe. ICML, 2020. [theoretical]
• Regularization Shortcomings for Continual Learning by Timothée Lesort, Andrei Stoian and David Filliat. arXiv, 2020. [fashion] [mnist]
• Strategies for Improving Single-Head Continual Learning Performance by Alaa El Khatib and Fakhri Karray. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 452--460, 2019. [cifar] [mnist]
• Towards Robust Evaluations of Continual Learning by Sebastian Farquhar and Yarin Gal. Privacy in Machine Learning and Artificial Intelligence Workshop, ICML, 2019. [fashion] [framework]
• Don't Forget, There Is More than Forgetting: New Metrics for Continual Learning by Natalia Díaz-Rodr\ǵuez, Vincenzo Lomonaco, David Filliat and Davide Maltoni. arXiv, 2018. [cifar] [framework]
• Three Scenarios for Continual Learning by Gido M van de Ven and Andreas S Tolias. Continual Learning Workshop NeurIPS, 2018. [framework] [mnist]

Neuroscience

7 papers

In this section we maintain a list of all Neuroscience papers that can be related (and useful) for continual machine learning.

• Biological Underpinnings for Lifelong Learning Machines by Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou and Hava Siegelmann. Nature Machine Intelligence, 196--210, 2022.
• Neural Inhibition for Continual Learning and Memory by and Helen C Barron. Current Opinion in Neurobiology, 85--94, 2021.
• Can Sleep Protect Memories from Catastrophic Forgetting? by Oscar C Gonzalez, Yury Sokolov, Giri Krishnan and Maxim Bazhenov. bioRxiv, 569038, 2019.
• Synaptic Consolidation: An Approach to Long-Term Learning by and Claudia Clopath. Cognitive Neurodynamics, 251--257, 2012. [hebbian]
• [The Organization of Behavior: A Neuropsychological Theory](https://www.amazon.com/Organization-Behavior-Neuropsychological-Theory/dp/0805843000 https://books.google.it/books/about/The_Organization_of_Behavior.html?id=ddB4AgAAQBAJ&printsec=frontcover&source=kp_read_button&redir_esc=y#v=onepage&q&f=false) by and D O Hebb. Lawrence Erlbaum, 2002. [hebbian]
• Negative Transfer Errors in Sequential Cognitive Skills: Strong-but-wrong Sequence Application. by Dan J. Woltz, Michael K. Gardner and Brian G. Bell. Journal of Experimental Psychology: Learning, Memory, and Cognition, 601--625, 2000.
• Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions. by and R Ratcliff. Psychological review, 285--308, 1990.

Others

47 papers

In this section we list all the other papers not appearing in at least one of the above sections.

• Dataset Knowledge Transfer for Class-Incremental Learning without Memory by Habib Slim, Eden Belouadah, Adrian Popescu and Darian M. Onchis. IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, Waikoloa, HI, USA, January 3-8, 2022, 3311--3320, 2022.
• Continual Novelty Detection by Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin and Richard E. Turner. arXiv, 2021.
• Co$2̂$L: Contrastive Continual Learning by Hyuntak Cha, Jaeho Lee and Jinwoo Shin. arXiv, 2021.
• Sustainable Artificial Intelligence through Continual Learning by Andrea Cossu, Marta Ziosi and Vincenzo Lomonaco. International Conference on AI for People (CAIP), 2021.
• Continual Backprop: Stochastic Gradient Descent with Persistent Randomness by Shibhansh Dohare, A. Rupam Mahmood and Richard S. Sutton. arXiv, 2021.
• Continuum: Simple Management of Complex Continual Learning Scenarios by Arthur Douillard and Timothée Lesort. arXiv, 2021.
• Posterior Meta-Replay for Continual Learning by Christian Henning, Maria Cervera, Francesco D'Angelo, Johannes Von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe and Joao Sacramento. Advances in Neural Information Processing Systems, 2021. [bayes]
• Rethinking the Representational Continuity: Towards Unsupervised Continual Learning by Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu and Sung Ju Hwang. International Conference on Learning Representations, 2021.
• Representation Memorization for Fast Learning New Knowledge without Forgetting by Fei Mi, Tao Lin and Boi Faltings. arXiv, 2021. [hebbian] [rnn]
• Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference by Martin Mundt, Iuliia Pliushch and Visvanathan Ramesh. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3523--3532, 2021.
• Active Class Incremental Learning for Imbalanced Datasets by Eden Belouadah, Adrian Popescu, Umang Aggarwal and Léo Saci. Computer Vision - ECCV 2020 Workshops - Glasgow, UK, August 23-28, 2020, Proceedings, Part VI, 146--162, 2020.
• Initial Classifier Weights Replay for Memoryless Class Incremental Learning by Eden Belouadah, Adrian Popescu and Ioannis Kanellos. 31st British Machine Vision Conference 2020, BMVC 2020, Virtual Event, UK, September 7-10, 2020, 2020.
• Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning by Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang and Zhangyang Wang. International Conference on Learning Representations, 2020.
• Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis by Tyler L Hayes and Christopher Kanan. CLVision Workshop at CVPR 2020, 1--15, 2020. [core50] [imagenet]
• Continual Learning with Bayesian Neural Networks for Non-Stationary Data by Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt and Stephan Günnemann. Eighth International Conference on Learning Representations, 2020. [bayes]
• Energy-Based Models for Continual Learning by Shuang Li, Yilun Du, Gido M. van de Ven, Antonio Torralba and Igor Mordatch. arXiv, 2020. [cifar] [experimental] [mnist]
• Continual Learning Using Task Conditional Neural Networks by Honglin Li, Payam Barnaghi, Shirin Enshaeifar and Frieder Ganz. arXiv, 2020. [cifar] [mnist]
• Mnemonics Training: Multi-Class Incremental Learning without Forgetting by Yaoyao Liu, An-An Liu, Yuting Su, Bernt Schiele and Qianru Sun. arXiv, 2020. [cifar] [imagenet]
• Continual Universal Object Detection by Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika and Stefano Soatto. arXiv, 2020.
• Gradient Projection Memory for Continual Learning by Gobinda Saha and Kaushik Roy. International Conference on Learning Representations, 2020.
• Structured Compression and Sharing of Representational Space for Continual Learning by Gobinda Saha, Isha Garg, Aayush Ankit and Kaushik Roy. arXiv, 2020. [cifar] [mnist]
• Gated Linear Networks by Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt and Marcus Hutter. arXiv, 2020.
• Lifelong Graph Learning by Chen Wang, Yuheng Qiu and Sebastian Scherer. arXiv, 2020. [graph]
• Superposition of Many Models into One by Brian Cheung, Alex Terekhov, Yubei Chen, Pulkit Agrawal and Bruno Olshausen. arXiv, 2019. [cifar] [mnist]
• Continual Learning in Practice by Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle and Neil Lawrence. arXiv, 2019.
• Dynamically Constraining Connectionist Networks to Produce Distributed, Orthogonal Representations to Reduce Catastrophic Interference by and Robert French. Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, 335--340, 2019.
• Continual Learning via Neural Pruning by Siavash Golkar, Michael Kagan and Kyunghyun Cho. arXiv, 2019. [cifar] [mnist] [sparsity]
• BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach by Anna Kuzina, Evgenii Egorov and Evgeny Burnaev. arXiv, 2019. [bayes] [fashion] [mnist]
• Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild by Kibok Lee, Kimin Lee, Jinwoo Shin and Honglak Lee. Proceedings of the IEEE International Conference on Computer Vision, 312--321, 2019.
• Continual Learning Using Bayesian Neural Networks by HongLin Li, Payam Barnaghi, Shirin Enshaeifar and Frieder Ganz. arXiv, 2019. [bayes] [mnist]
• Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition by Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Yong Won Hong and Visvanathan Ramesh. arXiv, 2019. [audio] [bayes] [fashion] [framework] [generative] [mnist] [vision]
• Continual Rare-Class Recognition with Emerging Novel Subclasses by Hung Nguyen, Xuejian Wang and Leman Akoglu. ECML, 2019. [nlp]
• Random Path Selection for Incremental Learning by Jathushan Rajasegaran, Munawar Hayat, Salman Khan Fahad, Shahbaz Khan and Ling Shao. NeurIPS, 12669--12679, 2019. [cifar] [imagenet] [mnist]
• Improving and Understanding Variational Continual Learning by Siddharth Swaroop, Cuong V Nguyen, Thang D Bui and Richard E Turner. Continual Learning Workshop NeurIPS, 1--17, 2019. [bayes] [mnist]
• Continual Learning via Online Leverage Score Sampling by Dan Teng and Sakyasingha Dasgupta. arXiv, 2019. [cifar] [mnist]
• Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers by Xin Ye and Qiuyu Zhu. IEEE Access, 42024--42031, 2019. [cifar] [mnist]
• Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies by Alessandro Achille, Tom Eccles, Loic Matthey, Christopher P. Burgess, Nick Watters, Alexander Lerchner and Irina Higgins. Neural Information Processing Systems (NeurIPS), 2018.
• DeeSIL: Deep-Shallow Incremental Learning by Eden Belouadah and Adrian Popescu. Computer Vision - ECCV 2018 Workshops - Munich, Germany, September 8-14, 2018, Proceedings, Part II, 151--157, 2018.
• A Unifying Bayesian View of Continual Learning by Sebastian Farquhar and Yarin Gal. NeurIPS Bayesian Deep Learning Workshop, 2018. [bayes] [cifar] [mnist]
• Overcoming Catastrophic Interference Using Conceptor-Aided Backpropagation by Xu He and Herbert Jaeger. ICLR, 2018. [mnist]
• Less-Forgetful Learning for Domain Expansion in Deep Neural Networks by Heechul Jung, Jeongwoo Ju, Minju Jung and Junmo Kim. Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
• Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights by Arun Mallya, Dillon Davis and Svetlana Lazebnik. ECCV, 72--88, 2018. [imagenet]
• Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks by Massimiliano Mancini, Elisa Ricci, Barbara Caputo and Samuel Rota Bulò. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 180--189, 2018. [sparsity] [vision]
• Variational Continual Learning by Cuong V Nguyen, Yingzhen Li, Thang D Bui and Richard E Turner. ICLR, 2018. [bayes]
• Task Agnostic Continual Learning Using Online Variational Bayes by Chen Zeno, Itay Golan, Elad Hoffer and Daniel Soudry. NeurIPS Bayesian Deep Learning Workshop, 2018. [bayes] [cifar] [mnist]
• [Encoder Based Lifelong Learning](http://arxiv.org/abs/1704.01920 http://dx.doi.org/10.1109/ICCV.2017.148) by Amal Rannen Triki, Rahaf Aljundi, Mathew B. Blaschko and Tinne Tuytelaars. Proceedings of the IEEE International Conference on Computer Vision, 1329--1337, 2017. [imagenet] [vision]
• Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios by Christoph Käding, Erik Rodner, Alexander Freytag and Joachim Denzler. ACCV Workshop, 2016. [imagenet]

Regularization Methods

29 papers

In this section we collect all the papers introducing a continual learning strategy employing some regularization methods.

• Using Hindsight to Anchor Past Knowledge in Continual Learning by Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip Torr and David Lopez-Paz. arXiv, 2021.
• Contrastive Continual Learning with Feature Propagation by Xuejun Han and Yuhong Guo. arXiv:2112.01713 [cs], 2021.
• Gradient Projection Memory for Continual Learning by Gobinda Saha, Isha Garg and Kaushik Roy. arXiv:2103.09762 [cs], 2021.
• Gradient Projection Memory for Continual Learning by Gobinda Saha, Isha Garg and Kaushik Roy. , 2021.
• Modeling the Background for Incremental Learning in Semantic Segmentation by Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci and Barbara Caputo. CVPR, 9233--9242, 2020.
• PLOP: Learning without Forgetting for Continual Semantic Segmentation by Arthur Douillard, Yifu Chen, Arnaud Dapogny and Matthieu Cord. arXiv, 2020.
• Insights from the Future for Continual Learning by Arthur Douillard, Eduardo Valle, Charles Ollion, Thomas Robert and Matthieu Cord. arXiv, 2020.
• PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning by Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert and Eduardo Valle. European Conference on Computer Vision (ECCV), 2020.
• Uncertainty-Guided Continual Learning with Bayesian Neural Networks by Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell and Marcus Rohrbach. ICLR, 2020. [bayes] [cifar] [fashion] [mnist]
• Continual Learning of Object Instances by Kishan Parshotam and Mert Kilickaya. CVPR 2020: Workshop on Continual Learning in Computer Vision, 2020. [vision]
• Efficient Continual Learning in Neural Networks with Embedding Regularization by Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco and Aurelio Uncini. Neurocomputing, 2020. [cifar] [mnist]
• Continual Learning with Hypernetworks by Johannes von Oswald, Christian Henning, João Sacramento and Benjamin F Grewe. International Conference on Learning Representations, 2020. [cifar] [mnist]
• Uncertainty-Based Continual Learning with Adaptive Regularization by Hongjoon Ahn, Sungmin Cha, Donggyu Lee and Taesup Moon. NeurIPS, 4392--4402, 2019. [bayes] [cifar] [mnist]
• Task-Free Continual Learning by Rahaf Aljundi, Klaas Kelchtermans and Tinne Tuytelaars. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [vision]
• Learning without Memorizing by Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu and Rama Chellappa. CVPR, 2019. [cifar]
• Incremental Learning Techniques for Semantic Segmentation by Umberto Michieli and Pietro Zanuttigh. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 3205--3212, 2019.
• Functional Regularisation for Continual Learning Using Gaussian Processes by Michalis K Titsias, Jonathan Schwarz, Alexander G de G Matthews, Razvan Pascanu and Yee Whye Teh. arXiv, 2019. [mnist] [omniglot]
• Memory Aware Synapses: Learning What (Not) to Forget by Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach and Tinne Tuytelaars. The European Conference on Computer Vision (ECCV), 2018. [vision]
• Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence by Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan and Philip H. S. Torr. Proceedings of the European Conference on Computer Vision (ECCV), 532--547, 2018.
• Rotate Your Networks: Better Weight Consolidation and Less Catastrophic Forgetting by Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M. Lopez and Andrew D Bagdanov. 2018 24th International Conference on Pattern Recognition (ICPR), 2262--2268, 2018. [cifar] [mnist]
• Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting by Hippolyt Ritter, Aleksandar Botev and David Barber. arXiv, 2018. [bayes] [mnist]
• Overcoming Catastrophic Forgetting with Hard Attention to the Task by Joan Serrà, D\d́ac Surís, Marius Miron and Alexandros Karatzoglou. ICML, 2018. [cifar] [fashion] [mnist]
• Overcoming Catastrophic Forgetting in Neural Networks by James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran and Raia Hadsell. PNAS, 3521--3526, 2017. [mnist]
• Overcoming Catastrophic Forgetting by Incremental Moment Matching by Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha and Byoung-Tak Zhang. Advances in Neural Information Processing Systems, 4653--4663, 2017. [bayes] [cifar] [mnist]
• Lifelong Generative Modeling by Jason Ramapuram, Magda Gregorova and Alexandros Kalousis. arXiv, 1--14, 2017. [fashion] [generative] [mnist]
• Continual Learning in Generative Adversarial Nets by Ari Seff, Alex Beatson, Daniel Suo and Han Liu. arXiv, 1--9, 2017. [mnist]
• Incremental Learning of Object Detectors without Catastrophic Forgetting by Konstantin Shmelkov, Cordelia Schmid and Karteek Alahari. Proceedings of the IEEE International Conference on Computer Vision, 3420--3429, 2017.
• Continual Learning Through Synaptic Intelligence by Friedemann Zenke, Ben Poole and Surya Ganguli. International Conference on Machine Learning, 3987--3995, 2017. [cifar] [mnist]
• Learning without Forgetting by Zhizhong Li and Derek Hoiem. European Conference on Computer Vision, 614--629, 2016. [imagenet]

Rehearsal Methods

29 papers

In this section we collect all the papers introducing a continual learning strategy employing some rehearsal methods.

• It's All About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning by Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo, Vincenzo Lomonaco and Davide Bacciu. , 2022.
• Foundational Models for Continual Learning: An Empirical Study of Latent Replay by Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish and Laurent Charlin. arXiv, 2022.
• Using Hindsight to Anchor Past Knowledge in Continual Learning by Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip Torr and David Lopez-Paz. arXiv, 2021.
• Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams by Matthias De Lange and Tinne Tuytelaars. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 8250--8259, 2021. [cifar] [framework] [mnist] [vision]
• Replay in Deep Learning: Current Approaches and Missing Biological Elements by Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski and Christopher Kanan. Neural Computation, 2908--2950, 2021.
• Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification by Muhammad Rifki Kurniawan, Xing Wei and Yihong Gong. arXiv, 2021.
• Distilled Replay: Overcoming Forgetting through Synthetic Samples by Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco and Davide Bacciu. 1st International Workshop on Continual Semi-Supervised Learning (CSSL) at IJCAI, 2021.
• Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning by Eli Verwimp, Matthias De Lange and Tinne Tuytelaars. Proceedings of the IEEE/CVF International Conference on Computer Vision, 9385--9394, 2021.
• Online Coreset Selection for Rehearsal-based Continual Learning by Jaehong Yoon, Divyam Madaan, Eunho Yang and Sung Ju Hwang. arXiv, 2021.
• CALM: Continuous Adaptive Learning for Language Modeling by Kristjan Arumae and Parminder Bhatia. arXiv, 2020. [nlp]
• ScaIL: Classifier Weights Scaling for Class Incremental Learning by Eden Belouadah and Adrian Popescu. IEEE Winter Conference on Applications of Computer Vision, WACV 2020, Snowmass Village, CO, USA, March 1-5, 2020, 1255--1264, 2020.
• REMIND Your Neural Network to Prevent Catastrophic Forgetting by Tyler L. Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya and Christopher Kanan. Proceedings of the 2020 ECCV, 2020.
• CLOPS: Continual Learning of Physiological Signals by Dani Kiyasseh, Tingting Zhu and David A Clifton. arXiv, 2020.
• Continual Learning with Bayesian Neural Networks for Non-Stationary Data by Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt and Stephan Günnemann. Eighth International Conference on Learning Representations, 2020. [bayes]
• GDumb: A Simple Approach That Questions Our Progress in Continual Learning by Ameya Prabhu, Philip H. S. Torr and Puneet K. Dokania. Computer Vision – ECCV 2020, 524--540, 2020.
• Graph-Based Continual Learning by Binh Tang and David S. Matteson. International Conference on Learning Representations, 2020.
• Brain-Inspired Replay for Continual Learning with Artificial Neural Networks by Gido M. van de Ven, Hava T. Siegelmann and Andreas S. Tolias. Nature Communications, 2020. [cifar] [framework] [generative] [mnist]
• Continual Learning with Hypernetworks by Johannes von Oswald, Christian Henning, João Sacramento and Benjamin F Grewe. International Conference on Learning Representations, 2020. [cifar] [mnist]
• Online Continual Learning with Maximal Interfered Retrieval by Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin and Lucas Page-Caccia. Advances in Neural Information Processing Systems 32, 11849--11860, 2019. [cifar] [mnist]
• Gradient Based Sample Selection for Online Continual Learning by Rahaf Aljundi, Min Lin, Baptiste Goujaud and Yoshua Bengio. Advances in Neural Information Processing Systems 32, 11816--11825, 2019. [cifar] [mnist]
• IL2M: Class Incremental Learning With Dual Memory by Eden Belouadah and Adrian Popescu. 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, 583--592, 2019.
• [On Tiny Episodic Memories in Continual Learning](https://github.com/facebookresearch/agem http://arxiv.org/abs/1902.10486) by Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip H S Torr and Marc'Aurelio Ranzato. arXiv, 2019. [cifar] [imagenet] [mnist]
• Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients by Yu Chen, Tom Diethe and Neil Lawrence. arXiv, 2019. [bayes]
• [Memory Efficient Experience Replay for Streaming Learning](https://github.com/tyler-hayes/ExStream. http://arxiv.org/abs/1809.05922) by Tyler L Hayes, Nathan D Cahill and Christopher Kanan. IEEE International Conference on Robotics and Automation (ICRA), 2019. [core50]
• Experience Replay for Continual Learning by David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P Lillicrap and Greg Wayne. NeurIPS, 350--360, 2019.
• Prototype Reminding for Continual Learning by Mengmi Zhang, Tao Wang, Joo Hwee Lim and Jiashi Feng. arXiv, 1--10, 2019. [bayes] [cifar] [imagenet] [mnist]
• Selective Experience Replay for Lifelong Learning by David Isele and Akansel Cosgun. Thirty-Second AAAI Conference on Artificial Intelligence, 3302--3309, 2018.
• iCaRL: Incremental Classifier and Representation Learning by Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl and Christoph H Lampert. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [cifar]
• Preventing Catastrophic Interference in MultipleSequence Learning Using Coupled Reverberating Elman Networks by Bernard Ans, Stephane Rousset, Robert M. French and Serban C. Musca. Proceedings of the 24th Annual Conference of the Cognitive Science Society, 2002. [rnn]

Review Papers and Books

24 papers

In this section we collect all the main review papers and books on continual learning and related subjects. These may constitute a solid starting point for continual learning newcomers.

• A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning by Umang Aggarwal, Adrian Popescu, Eden Belouadah and Céline Hudelot. arXiv, 2022.
• How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition by Jorge A. Mendez and Eric Eaton. , 2022.
• A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks by Eden Belouadah, Adrian Popescu and Ioannis Kanellos. Neural Networks, 38--54, 2021.
• Continual Learning for Recurrent Neural Networks: An Empirical Evaluation by Andrea Cossu, Antonio Carta, Vincenzo Lomonaco and Davide Bacciu. Neural Networks, 607--627, 2021. [rnn]
• A Continual Learning Survey: Defying Forgetting in Classification Tasks by Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh and Tinne Tuytelaars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [framework]
• Replay in Deep Learning: Current Approaches and Missing Biological Elements by Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski and Christopher Kanan. arXiv, 2021.
• Continual Lifelong Learning in Natural Language Processing: A Survey by Magdalena Biesialska, Katarzyna Biesialska and Marta R. Costa-jussà. Proceedings of the 28th International Conference on Computational Linguistics, 6523--6541, 2020. [nlp]
• Embracing Change: Continual Learning in Deep Neural Networks by Raia Hadsell, Dushyant Rao, Andrei A Rusu and Razvan Pascanu. Trends in Cognitive Sciences, 2020.
• Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges by Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat and Natalia Díaz-Rodr\ǵuez. Information Fusion, 52--68, 2020. [framework]
• A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning by Martin Mundt, Yong Won Hong, Iuliia Pliushch and Visvanathan Ramesh. arXiv, 32, 2020. [bayes] [framework]
• A Review of Off-Line Mode Dataset Shifts by Carla C. Takahashi and Antonio P. Braga. IEEE Computational Intelligence Magazine, 16--27, 2020.
• Continual Learning with Neural Networks: A Review by Abhijeet Awasthi and Sunita Sarawagi. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 362--365, 2019.
• Continual Lifelong Learning with Neural Networks: A Review by German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan and Stefan Wermter. Neural Networks, 54--71, 2019. [framework]
• [Lifelong Machine Learning, Second Edition](https://www.cs.uic.edu/ liub/lifelong-machine-learning.html) by Zhiyuan Chen and Bing Liu. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2018.
• Measuring Catastrophic Forgetting in Neural Networks by Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L Hayes and Christopher Kanan. Thirty-Second AAAI Conference on Artificial Intelligence, 2018. [mnist]
• Generative Models from the Perspective of Continual Learning by Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian and David Filliat. Proceedings of the International Joint Conference on Neural Networks, 2018. [cifar] [generative] [mnist]
• Incremental On-Line Learning: A Review and Comparison of State of the Art Algorithms by Viktor Losing, Barbara Hammer and Heiko Wersing. Neurocomputing, 1261--1274, 2018.
• A Comprehensive, Application-Oriented Study of Catastrophic Forgetting in DNNs by B Pfülb and A Gepperth. ICLR, 2018. [fashion] [mnist]
• Born to Learn: The Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks by Andrea Soltoggio, Kenneth O. Stanley and Sebastian Risi. Neural Networks, 48--67, 2018.
• Avoiding Catastrophic Forgetting by and Michael E. Hasselmo. Trends in Cognitive Sciences, 407--408, 2017.
• Lifelong Machine Learning: A Paradigm for Continuous Learning by and Bing Liu. Frontiers of Computer Science, 359--361, 2017.
• Learning in Nonstationary Environments: A Survey by Gregory Ditzler, Manuel Roveri, Cesare Alippi and Robi Polikar. IEEE Computational Intelligence Magazine, 12--25, 2015.
• Never-Ending Learning by Tom Mitchell, William W Cohen, E Hruschka, Partha P Talukdar, B Yang, Justin Betteridge, Andrew Carlson, B Dalvi, Matt Gardner, Bryan Kisiel, J Krishnamurthy, Ni Lao, K Mazaitis, T Mohamed, N Nakashole, E Platanios, A Ritter, M Samadi, B Settles, R Wang, D Wijaya, A Gupta, X Chen, A Saparov, M Greaves and J Welling. Communications of the Acm, 2302--2310, 2015.
• Catastrophic Forgetting; Catastrophic Interference; Stability; Plasticity; Rehearsal. by and Anthony Robins. Connection Science, 123--146, 1995. [dual]

Robotics

7 papers

In this section we maintain a list of all Robotics papers that can be related to continual learning.

• Online Continual Learning for Embedded Devices by Tyler L. Hayes and Christopher Kanan. arXiv, 2022.
• Controlling Soft Robotic Arms Using Continual Learning by Francesco Piqué, Hari Teja Kalidindi, Lorenzo Fruzzetti, Cecilia Laschi, Arianna Menciassi and Egidio Falotico. IEEE Robotics and Automation Letters, 5469--5476, 2022.
• Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot by Ali Ayub and Alan R. Wagner. arXiv, 2020.
• Online Object and Task Learning via Human Robot Interaction by M. Dehghan, Z. Zhang, M. Siam, J. Jin, L. Petrich and M. Jagersand. 2019 International Conference on Robotics and Automation (ICRA), 2019.
• Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics by and Jay M Wong. arXiv, 2016.
• A Lifelong Learning Perspective for Mobile Robot Control by and Sebastian Thrun. Intelligent Robots and Systems, 201--214, 1995.
• Explanation-Based Neural Network Learning for Robot Control by Tom M Mitchell and Sebastian B Thrun. Advances in Neural Information Processing Systems 5, 1993.