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b/joss.05093/10.21105.joss.05093.crossref.xml @@ -0,0 +1,408 @@ + + + + 20230516T143530-36b303a24b1f3545f0add9c751b1ac586536da7f + 20230516143530 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org/ + + + + + 05 + 2023 + + + 8 + + 85 + + + + Multi-view-AE: A Python package for multi-view +autoencoder models + + + + Ana Lawry + Aguila + https://orcid.org/0000-0003-0727-3274 + + + Alejandra + Jayme + + + Nina + Montaña-Brown + https://orcid.org/0000-0001-5685-971X + + + Vincent + Heuveline + + + Andre + Altmann + https://orcid.org/0000-0002-9265-2393 + + + + 05 + 16 + 2023 + + + 5093 + + + 10.21105/joss.05093 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.7871099 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5093 + + + + 10.21105/joss.05093 + https://joss.theoj.org/papers/10.21105/joss.05093 + + + https://joss.theoj.org/papers/10.21105/joss.05093.pdf + + + + + + Multiview learning in biomedical +applications + Serra + Artificial intelligence in the age of neural +networks and brain computing + 10.1016/B978-0-12-815480-9.00013-X + 2019 + Serra, A., Galdi, P., & +Tagliaferri, R. 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Deep multi-view representation +learning for video anomaly detection using spatiotemporal autoencoders. +Circuits, Systems, and Signal Processing, 40. +https://doi.org/10.1007/s00034-020-01522-7 + + + PyTorch: An imperative style, +high-performance deep learning library + Paszke + Advances in neural information processing +systems 32 + 2019 + Paszke, A., Gross, S., Massa, F., +Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, +N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, +M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. +(2019). PyTorch: An imperative style, high-performance deep learning +library. In Advances in neural information processing systems 32 (pp. +8024–8035). Curran Associates, Inc. +https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf + + + Pytorch lightning + Falcon + GitHub. Note: +https://github.com/PyTorchLightning/pytorch-lightning + 6 + 3 + 2019 + Falcon, W., & others. (2019). +Pytorch lightning. GitHub. Note: +Https://Github.com/PyTorchLightning/Pytorch-Lightning, +3(6). + + + + + + From 24e5e02c24e71f8b580a1b1676af0482923deb43 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Tue, 16 May 2023 15:36:03 +0100 Subject: [PATCH 3/6] Creating 10.21105.joss.05093.jats --- joss.05093/10.21105.joss.05093.jats | 849 ++++++++++++++++++++++++++++ 1 file changed, 849 insertions(+) create mode 100644 joss.05093/10.21105.joss.05093.jats diff --git a/joss.05093/10.21105.joss.05093.jats b/joss.05093/10.21105.joss.05093.jats new file mode 100644 index 0000000000..216e3e26e6 --- /dev/null +++ b/joss.05093/10.21105.joss.05093.jats @@ -0,0 +1,849 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5093 +10.21105/joss.05093 + +Multi-view-AE: A Python package for multi-view +autoencoder models + + + +https://orcid.org/0000-0003-0727-3274 + +Aguila +Ana Lawry + + +* + + + +Jayme +Alejandra + + + + +https://orcid.org/0000-0001-5685-971X + +Montaña-Brown +Nina + + + + + +Heuveline +Vincent + + + + +https://orcid.org/0000-0002-9265-2393 + +Altmann +Andre + + + + + +Centre for Medical Image Computing (CMIC), Medical Physics +and Biomedical Engineering, University College London (UCL), London, +UK + + + + +Engineering Mathematics and Computing Lab (EMCL), +Heidelberg, Germany + + + + +* E-mail: + + +25 +11 +2022 + +8 +85 +5093 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Autoencoders +Multi-view +Unsupervised learning +Representation learning +Data generation + + + + +

Often, data can be naturally described via multiple views or +modalities. For example, we could consider an image and the +corresponding text as different modalities. These modalities contain +complementary information which can be modelled jointly using multi-view +methods. The joint modelling of multiple modalities has been explored in +many research fields such as medical imaging +(Serra +et al., 2019), chemistry +(Sjöström +et al., 1983), and natural language processing +(Sadr +et al., 2020).

+

Autoencoders are unsupervised models which learn low dimensional +latent representations of complex data. The autoencoder framework +consists of two mappings; the encoder which embeds information from the +input space into a latent space, and a decoder which transforms point +estimates from the latent space back into in the input space. +Autoencoders have been successful in downstream tasks such as +classification +(Creswell +& Bharath, 2017), outlier detection +(An +& Cho, 2015), and data generation +(Wei +& Mahmood, 2021).

+

There exist many software frameworks for extending autoencoders to +multiple modalities. Generally, this involves learning separate encoder +and decoder functions for each modality with the latent representations +being combined or associated in some way. By far the most popular group +of multi-view autoencoder models are multi-view extensions of +Variational Autoencoders (VAEs) where the latent space is regularised by +mapping the encoding distributions to a gaussian prior using a +Kullback–Leibler (KL) divergence term. However, there are also other +multi-view autoencoder frameworks, such as multi-view Adversarial +Autoencoders (AAEs) +(X. +Wang et al., 2019). Here the latent space is regularised by +mapping the encoding distribution to a prior (here a gaussian) using an +auxiliary discriminator tasked with distinguishing samples from the +posterior and prior distributions. The choice of AAE or VAE model may be +influenced by various elements of the application process. For example, +the encoding distribution which best describes the data or stability +during training may impact the choice of model.

+ +

Single view autoencoder frameworks; (a) vanilla + autoencoder, (b) adversarial autoencoder, (c) variational + autoencoder.

+ +
+

Even within these regularisation frameworks there are vast modelling +differences to be considered when choosing the best model for the task +at hand. +[fig:AElatent] +depicts two possible latent variable models for modelling two views of +data; + +X +and + +Y. +[fig:AElatent]a +shows the joint latent variable model +(Suzuki +& Matsuo, 2022) where both views, + + +X +and + +Y, +share an underlying factor. The latent variable model in +[fig:AElatent]b +shows a coordinated model, which assumes some relationship between the +latent variables, + +zx +and + +zy +of + +X +and + +Y +respectively. Which latent variable model is most appropriate depends on +the desired outcome of the learning task. Example multi-view autoencoder +frameworks built for these two latent variable models are given in +[fig:AEexample].

+ +

Latent variable models for two input views. Latent + variable model where data + + X + and + + Y + (a) share an underlying latent factor + + z + (b) have associated latent factors + + zx + and + + zy.

+ +
+ +

Example frameworks of a two-view autoencoder for data + + + X + and + + Y + for a (a) joint model, where the individual latent spaces are combined + and the reconstruction is carried out from the joint latent space, and + a (b) coordinated model, where the latent representations are + coordinated either by cross view generation or an addition loss term + for association between the latent + variables.

+ +
+

Given the large number of multi-view autoencoders and versatility of +architecture, it is important to consider which model would best suit +the use case. multi-view-AE is a Python library +which implements several variants of multi-view autoencoders in a +simple, flexible, and easy-to-use framework. We would like to highlight +the following benefits of our package.

+

Firstly, the multi-view-AE package is +implemented with a similar interface to +scikit-learn +(Buitinck +et al., 2013) with common and straight-forward functions +implemented for all models. This makes it simple for users to train and +evaluate models without requiring detailed knowledge of the methodology. +Secondly, all models follow a modular structure. This gives users the +flexibility to choose the class (such as the encoder or decoder network) +from the available implementations, or to contribute their own. As such, +the multi-view-AE package is accessible to both +beginners, with off-the-shelf models, and experts, who wish to adapt the +existing framework for further research purposes. Finally, the +multi-view-AE package uses the +PyTorch-Lightning +(Falcon +& others, 2019) API which offers the same functionality as +raw PyTorch +(Paszke +et al., 2019) in a more structured and streamlined way. This +offers users more flexibility, faster training and optimisation time, +and high scalability.

+ + Statement of need +

Multi-view autoencoders have become a popular family of + unsupervised learning methods in the field of multi-view learning. The + flexibility of the form of the encoder and decoder functions, ease of + extension to multiple views, generative properties, and adaptability + to large scale datasets has contributed to the popularity of + multi-view autoencoders compared to other multi-view methods. + Subsequently, multi-view autoencoders have used to address challenges + across a range of fields; such as anomaly detection from videos + (Deepak + et al., 2021) or cross-modal generation of multi-omics data + (“A + Mixture-of-Experts Deep Generative Model for Integrated Analysis of + Single-Cell Multiomics Data,” 2021).

+

There exist many different multi-view autoencoder frameworks with + the best method of choice depending on the specific task. Existing + code is often implemented using different Deep Learning frameworks or + varied programming styles making it difficult for users to compare + methods. The motivation for developing the + multi-view-AE library is to widen the + accessibility of these algorithms by allowing users to easily test + methods on new datasets and enable developers to compare methods and + extend code for new research applications. The modular structure of + the multi-view-AE library allows developers to + choose which element of the code to extend and swap out existing + Python classes for new implementations whilst leaving the wider + codebase untouched.

+

There exists, as far as we are aware, no Python library that + collates a large number of multi-view autoencoder models into one easy + to use framework. The Pixyz library + (Suzuki + et al., 2021) is probably the closest relative of + multi-view-AE, implementing a number of + multi-view autoencoder methods. However, Pixyz + is designed for the wider field of deep generative modelling whereas + multi-view-AE focuses specifically on + multi-view autoencoder models. As such + multi-view-AE builds upon + Pixyz’s multi-view offering providing a wider + range of multi-view methods.

+
+ + Software description + + Software architecture +

Following the scikit-learn interface, to + train a multi-view autoencoder model with the + multi-view-AE package, first a model object + is initialised with relevant parameters in an easy-to-configure + file. Next, the model is trained with the + fit() method using the specified data. + Following fitting, the saved model object can be used for further + analysis: predicting the latent variables, using + predict_latent(), or data reconstructions, + using predict_reconstruction().

+

All models are implemented in PyTorch + using the PyTorch-Lightning wrapper.

+
+ + Parameter settings +

The multi-view-AE package uses the Hydra + API for configuration management. Most parameters are set in a + configuration file and are loaded into the model object by + Hydra. The combination of Hydra with the + modular structure of models in the + multi-view-AE package, makes it easy for the + user to replace model elements with, either other available + implementations or their own by editing the relevant section of the + configuration file.

+
+ + Implemented models +

A complete model list at the time of publication:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model classModel nameNumber of views
mcVAEMulti-Channel Variational Autoencoder + (mcVAE) + (Antelmi + et al., 2019)>=1
AEMulti-view Autoencoder>=1
AAEMulti-view Adversarial Autoencoder with + separate latent representations>=1
DVCCADeep Variational CCA + (W. + Wang et al., 2016)2
jointAAEMulti-view Adversarial Autoencoder with + joint latent representation>=1
wAAEMulti-view Adversarial Autoencoder with + joint latent representation and Wasserstein loss>=1
mmVAEVariational mixture-of-experts + autoencoder (MMVAE) + (Shi + et al., 2019)>=1
mVAEMultimodal Variational Autoencoder (MVAE) + (Wu + & Goodman, 2018)>=1
me_mVAEMultimodal Variational Autoencoder (MVAE) + with separate ELBO terms for each view + (Wu + & Goodman, 2018)>=1
JMVAEJoint Multimodal Variational + Autoencoder(JMVAE-kl) + (Suzuki + et al., 2016)2
MVTCAEMulti-View Total Correlation Auto-Encoder + (MVTCAE) + (Hwang + et al., 2021)>=1
MoPoEVAEMixture-of-Products-of-Experts VAE + (T. + M. Sutter et al., 2021)>=1
mmJSDMultimodal Jensen-Shannon divergence + model (mmJSD) + (T. + Sutter et al., 2021)>=1
+
+
+ + Documentation +

Documentation is available + (https://multi-view-ae.readthedocs.io/en/latest/) for the + multi-view-AE package as well as tutorial + notebooks. These resources serve as both guides to the + multi-view-AE package and educational + material for multi-view autoencoder models.

+
+
+ + Acknowledgements +

We would like to thank Thomas Sutter, HyeongJoo Hwang and, Marco + Lorenzi, and Brooks Paige, for their help understanding the + Mixture-of-Product-of-Experts VAE + (T. + M. Sutter et al., 2021) and mmJSD + (T. + Sutter et al., 2021), MVTCAE + (Hwang + et al., 2021), mcVAE + (Antelmi + et al., 2019), and MMVAE + (Shi et + al., 2019) models, respectively.

+

ALA and NMB are supported by the EPSRC-funded UCL Centre for + Doctoral Training in Intelligent, Integrated Imaging in Healthcare + (i4health) and the Department of Health’s NIHR-funded Biomedical + Research Centre at University College London Hospitals (EP/S021930/1). + AJ is supported by the Engineering Mathematics and Computing Lab + (EMCL), Heidelberg University, the Helmholtz Association under the + joint research school “HIDSS4Health – Helmholtz Information and Data + Science School for Health”, and the Heidelberg Institute for + Theoretical Studies (HITS).

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