demo/embedding_demo/EmbeddingDemo.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yaml\n",
    "\n",
    "import cufflinks as cf\n",
    "import plotly.offline\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import SpectralEmbedding, TSNE, LocallyLinearEmbedding, MDS\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "config = yaml.safe_load(open('../../conf/conf.yaml'))\n",
    "data_path=os.path.join(config['DATA_PATH'],'embedding_results')\n",
    "\n",
    "# load supernode embedding and metadata:\n",
    "def load_emb_data(tag, data_path=data_path):\n",
    "    #emb=pd.read_csv('dcc_supernodes_combo.csv',header=None,sep='\\t')\n",
    "    emb=np.loadtxt(os.path.join(data_path,'dcc_supernodes_{}.csv'.format(tag)))\n",
    "    meta=pd.read_csv(os.path.join(data_path,'dcc_supernodes_labels_{}.txt'.format(tag)),encoding='latin1',sep='\\t')\n",
    "    return(emb,meta)\n",
    "\n",
    "### generate visualization\n",
    "def visualize(emb,meta,method='TSNE',dim=2, n_iter=300, title=\"\"):\n",
    "    if method == 'TSNE':   \n",
    "        tsne = TSNE(n_components=dim, verbose=1, perplexity=5, n_iter=n_iter, init='pca')\n",
    "        results = tsne.fit_transform(emb)\n",
    "    elif method == 'PCA':\n",
    "        pca = PCA(n_components=dim)\n",
    "        results=pca.fit_transform(emb)\n",
    "    elif method == 'SpectralEmbedding':\n",
    "        speb = SpectralEmbedding(n_components=dim)\n",
    "        results = speb.fit_transform(emb)\n",
    "    elif method == 'MDS':\n",
    "        mds=MDS(n_components=dim)\n",
    "        results = mds.fit_transform(emb)\n",
    "    elif method == 'LLE':\n",
    "        lle = LocallyLinearEmbedding(n_components=dim)\n",
    "        results = lle.fit_transform(emb)\n",
    "    else:\n",
    "        print('Unknown method')\n",
    "        return\n",
    "    meta['x']=results[:,0]\n",
    "    meta['y']=results[:,1]\n",
    "    if(dim==2):\n",
    "        meta.iplot(kind='scatter',x=\"x\",y=\"y\", text=\"Title\", categories=\"Conference\", title=title)\n",
    "    elif(dim==3):\n",
    "        meta['z']=results[:,2]\n",
    "        meta.iplot(kind='scatter3d',x=\"x\",y=\"y\",z=\"z\", text=\"Title\", categories=\"Conference\", title=title)\n",
    "    else:\n",
    "        print('Dimension is not right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Embeddings of Single Modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 16 nearest neighbors...\n",
      "[t-SNE] Indexed 410 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 410 samples in 0.006s...\n",
      "[t-SNE] Computed conditional probabilities for sample 410 / 410\n",
      "[t-SNE] Mean sigma: 0.442954\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 82.101074\n",
      "[t-SNE] KL divergence after 500 iterations: 0.958247\n"
     ]
    },
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          "Generative Neural Machine Translation",
          "Banach Wasserstein GAN",
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          "Train longer, generalize better: closing the generalization gap in large batch training of neural networks",
          "The Numerics of GANs",
          "Stabilizing Training of Generative Adversarial Networks through Regularization",
          "Training Deep Networks without Learning Rates Through Coin Betting",
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          "Fisher GAN",
          "Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System",
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          "ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games",
          "Dual Discriminator Generative Adversarial Nets",
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          "Hierarchical Attentive Recurrent Tracking",
          "Learning to Compose Domain-Specific Transformations for Data Augmentation",
          "Language Modeling with Recurrent Highway Hypernetworks",
          "Bayesian Compression for Deep Learning",
          "Streaming Sparse Gaussian Process Approximations",
          "Spherical convolutions and their application in molecular modelling",
          "Neural system identification for large populations separating what and where",
          "A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning",
          "Bayesian GAN",
          "Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks",
          "Diving into the shallows: a computational perspective on large-scale shallow learning",
          "InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations",
          "Deep Hyperspherical Learning",
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          "Streaming Weak Submodularity: Interpreting Neural Networks on the Fly",
          "Prototypical Networks for Few-shot Learning",
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          "Dual Path Networks",
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          "A Unified Approach to Interpreting Model Predictions",
          "Context Selection for Embedding Models",
          "Working hard to know your neighbor's margins: Local descriptor learning loss",
          "Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon",
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   "source": [
    "### Text2Graph\n",
    "emb,meta=load_emb_data('t2g')\n",
    "visualize(emb,meta,method='TSNE',dim=2, n_iter=500, title='Text2Graph (TSNE)')"
   ]
  },
  {
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   "execution_count": 8,
   "metadata": {},
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     "text": [
      "[t-SNE] Computing 16 nearest neighbors...\n",
      "[t-SNE] Indexed 213 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 213 samples in 0.004s...\n",
      "[t-SNE] Computed conditional probabilities for sample 213 / 213\n",
      "[t-SNE] Mean sigma: 0.603516\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 72.060509\n",
      "[t-SNE] KL divergence after 300 iterations: 0.882869\n"
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   "source": [
    "### Image2Graph\n",
    "emb,meta=load_emb_data('i2g')\n",
    "visualize(emb,meta,method='TSNE',dim=2, title='Image2Graph (TSNE)')"
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      "[t-SNE] Computing 16 nearest neighbors...\n",
      "[t-SNE] Indexed 32 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 32 samples in 0.002s...\n",
      "[t-SNE] Computed conditional probabilities for sample 32 / 32\n",
      "[t-SNE] Mean sigma: 4.091255\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 68.172462\n",
      "[t-SNE] KL divergence after 500 iterations: 0.705384\n"
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   ],
   "source": [
    "### Code2Graph (on subset of repositories)\n",
    "emb,meta=load_emb_data('c2g')\n",
    "visualize(emb,meta,method='TSNE',dim=2, n_iter=500, title='Code2Graph (TSNE)')"
   ]
  },
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   "metadata": {},
   "source": [
    "## Visualizing Embedding of the Combined Graph "
   ]
  },
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   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[t-SNE] Computing 16 nearest neighbors...\n",
      "[t-SNE] Indexed 654 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 654 samples in 0.012s...\n",
      "[t-SNE] Computed conditional probabilities for sample 654 / 654\n",
      "[t-SNE] Mean sigma: 0.000000\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 71.061691\n",
      "[t-SNE] KL divergence after 300 iterations: 1.110954\n"
     ]
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          "Fast Algorithms for Convolutional Neural Networks",
          "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network",
          "CleanNet: Transfer Learning for Scalable Image Classifier Training With Label Noise",
          "Hierarchical Novelty Detection for Visual Object Recognition",
          "Recurrent Convolutional Neural Network for Object Recognition",
          "Deep Marching Cubes: Learning Explicit Surface Representations",
          "Conditional Image-to-Image Translation",
          "Inverse Compositional Spatial Transformer Networks",
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          "ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing",
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          "Convolutional Sequence to Sequence Model for Human Dynamics",
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          "Learning Intrinsic Image Decomposition From Watching the World",
          "MegaDepth: Learning Single-View Depth Prediction From Internet Photos",
          "SO-Net: Self-Organizing Network for Point Cloud Analysis",
          "Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization",
          "Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification",
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          "Boosting Domain Adaptation by Discovering Latent Domains",
          "On Human Motion Prediction Using Recurrent Neural Networks",
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          "Learning Dual Convolutional Neural Networks for Low-Level Vision",
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          "Learning Latent Super-Events to Detect Multiple Activities in Videos",
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          "Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling",
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          "Rethinking the Inception Architecture for Computer Vision",
          "Spatially-Adaptive Filter Units for Deep Neural Networks",
          "Image Super-Resolution via Deep Recursive Residual Network",
          "Multiple Instance Detection Network With Online Instance Classifier Refinement",
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          "Towards Effective Low-Bitwidth Convolutional Neural Networks",
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          "In-Place Activated BatchNorm for Memory-Optimized Training of DNNs",
          "One-Shot Video Object Segmentation",
          "Neural Sign Language Translation",
          "HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN",
          "Partial Transfer Learning With Selective Adversarial Networks",
          "Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields",
          "Visual Question Reasoning on General Dependency Tree",
          "Annotating Object Instances With a Polygon-RNN",
          "Pyramid Stereo Matching Network",
          "AON: Towards Arbitrarily-Oriented Text Recognition",
          "Cascaded Pyramid Network for Multi-Person Pose Estimation",
          "Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs",
          "Knowledge Aided Consistency for Weakly Supervised Phrase Grounding",
          "LiDAR-Video Driving Dataset: Learning Driving Policies Effectively",
          "Multi-View 3D Object Detection Network for Autonomous Driving",
          "StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation",
          "Xception: Deep Learning With Depthwise Separable Convolutions",
          "Lip Reading Sentences in the Wild",
          "VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization",
          "Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning",
          "Learning to Evaluate Image Captioning",
          "ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans",
          "Eye In-Painting With Exemplar Generative Adversarial Networks",
          "Style Aggregated Network for Facial Landmark Detection",
          "Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors",
          "Structured Uncertainty Prediction Networks",
          "Low-Shot Learning With Large-Scale Diffusion",
          "GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning",
          "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation",
          "Who Let the Dogs Out? Modeling Dog Behavior From Visual Data",
          "Robust Physical-World Attacks on Deep Learning Visual Classification",
          "AMNet: Memorability Estimation With Attention",
          "A Point Set Generation Network for 3D Object Reconstruction From a Single Image",
          "End-to-End Learning of Motion Representation for Video Understanding",
          "Spatially Adaptive Computation Time for Residual Networks",
          "Compact Bilinear Pooling",
          "Learning Generative ConvNets via Multi-Grid Modeling and Sampling",
          "Dynamic Few-Shot Visual Learning Without Forgetting",
          "Social GAN: Socially Acceptable Trajectories With Generative Adversarial Networks",
          "DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data",
          "Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks",
          "Deep Pyramidal Residual Networks",
          "Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?",
          "Deep Back-Projection Networks for Super-Resolution",
          "Deep Residual Learning for Image Recognition",
          "Deeply Supervised Salient Object Detection With Short Connections",
          "CondenseNet: An Efficient DenseNet Using Learned Group Convolutions",
          "DeepMVS: Learning Multi-View Stereopsis",
          "Densely Connected Convolutional Networks",
          "Learning Deep Representation for Imbalanced Classification",
          "Real-Time Neural Style Transfer for Videos",
          "Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors",
          "Pointwise Convolutional Neural Networks",
          "Fast and Accurate Single Image Super-Resolution via Information Distillation Network",
          "LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation",
          "Deep 360 Pilot: Learning a Deep Agent for Piloting Through 360deg Sports Videos",
          "FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling",
          "Finding Tiny Faces",
          "Learning Answer Embeddings for Visual Question Answering",
          "Modeling Relationships in Referential Expressions With Compositional Modular Networks",
          "Image Specificity",
          "DenseCap: Fully Convolutional Localization Networks for Dense Captioning",
          "Image Generation From Scene Graphs",
          "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies",
          "Perturbative Neural Networks",
          "End-to-End Recovery of Human Shape and Pose",
          "Convolutional Neural Networks for No-Reference Image Quality Assessment",
          "Geometric Loss Functions for Camera Pose Regression With Deep Learning",
          "Simple Does It: Weakly Supervised Instance and Semantic Segmentation",
          "Accurate Image Super-Resolution Using Very Deep Convolutional Networks",
          "Context Embedding Networks",
          "Deeply-Recursive Convolutional Network for Image Super-Resolution",
          "Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled",
          "RON: Reverse Connection With Objectness Prior Networks for Object Detection",
          "Surface Networks",
          "A Hierarchical Approach for Generating Descriptive Image Paragraphs",
          "Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks",
          "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution",
          "Deep Learning Under Privileged Information Using Heteroscedastic Dropout",
          "TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks",
          "Fast Algorithms for Convolutional Neural Networks",
          "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network",
          "CleanNet: Transfer Learning for Scalable Image Classifier Training With Label Noise",
          "Hierarchical Novelty Detection for Visual Object Recognition",
          "Recurrent Convolutional Neural Network for Object Recognition",
          "Deep Marching Cubes: Learning Explicit Surface Representations",
          "Conditional Image-to-Image Translation",
          "Inverse Compositional Spatial Transformer Networks",
          "RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation",
          "ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing",
          "Decoupled Networks",
          "Deep Supervised Hashing for Fast Image Retrieval",
          "FOTS: Fast Oriented Text Spotting With a Unified Network",
          "Future Frame Prediction for Anomaly Detection  A New Baseline",
          "PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection",
          "PlaneNet: Piece-Wise Planar Reconstruction From a Single RGB Image",
          "Predicting Salient Face in Multiple-Face Videos",
          "Recognizing Human Actions as the Evolution of Pose Estimation Maps",
          "Richer Convolutional Features for Edge Detection",
          "SphereFace: Deep Hypersphere Embedding for Face Recognition",
          "Convolutional Sequence to Sequence Model for Human Dynamics",
          "CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes",
          "Learning Intrinsic Image Decomposition From Watching the World",
          "MegaDepth: Learning Single-View Depth Prediction From Internet Photos",
          "SO-Net: Self-Organizing Network for Point Cloud Analysis",
          "Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization",
          "Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification",
          "Fully Convolutional Networks for Semantic Segmentation",
          "Deep Photo Style Transfer",
          "Discriminability Objective for Training Descriptive Captions",
          "Efficient Deep Learning for Stereo Matching",
          "Non-Local Deep Features for Salient Object Detection",
          "Neural Baby Talk",
          "Unsupervised Video Summarization With Adversarial LSTM Networks",
          "PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning",
          "Boosting Domain Adaptation by Discovering Latent Domains",
          "On Human Motion Prediction Using Recurrent Neural Networks",
          "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning",
          "Disentangled Person Image Generation",
          "Conditional Probability Models for Deep Image Compression",
          "Cross-Stitch Networks for Multi-Task Learning",
          "NAG: Network for Adversary Generation",
          "Beyond the Pixel-Wise Loss for Topology-Aware Delineation",
          "3D Bounding Box Estimation Using Deep Learning and Geometry",
          "Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering",
          "Human Pose Estimation With Parsing Induced Learner",
          "Video Frame Interpolation via Adaptive Convolution",
          "Learning Dual Convolutional Neural Networks for Low-Level Vision",
          "Towards Accurate Multi-Person Pose Estimation in the Wild",
          "Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach",
          "Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation",
          "Learning Latent Super-Events to Detect Multiple Activities in Videos",
          "Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes",
          "Learning Pose Specific Representations by Predicting Different Views",
          "Generative Adversarial Perturbations",
          "PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference",
          "Attentive Generative Adversarial Network for Raindrop Removal From a Single Image",
          "Few-Shot Image Recognition by Predicting Parameters From Activations",
          "Frustum PointNets for 3D Object Detection From RGB-D Data",
          "GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation",
          "Low-Shot Learning With Imprinted Weights",
          "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation",
          "Semi-Parametric Image Synthesis",
          "Top-Down Visual Saliency Guided by Captions",
          "Optical Flow Estimation Using a Spatial Pyramid Network",
          "Self-Critical Sequence Training for Image Captioning",
          "Cross-Domain Self-Supervised Multi-Task Feature Learning Using Synthetic Imagery",
          "End-To-End Instance Segmentation With Recurrent Attention",
          "Action Sets: Weakly Supervised Action Segmentation Without Ordering Constraints",
          "End-to-End Weakly-Supervised Semantic Alignment",
          "Real-World Repetition Estimation by Div, Grad and Curl",
          "Adversarially Learned One-Class Classifier for Novelty Detection",
          "Logo Synthesis and Manipulation With Clustered Generative Adversarial Networks",
          "Generate to Adapt: Aligning Domains Using Generative Adversarial Networks",
          "Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation",
          "Avatar-Net: Multi-Scale Zero-Shot Style Transfer by Feature Decoration",
          "Weakly Supervised Dense Video Captioning",
          "Pixels, Voxels, and Views: A Study of Shape Representations for Single View 3D Object Shape Prediction",
          "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network",
          "Learning From Simulated and Unsupervised Images Through Adversarial Training",
          "Actor and Observer: Joint Modeling of First and Third-Person Videos",
          "Deep Metric Learning via Facility Location",
          "VITAL: VIsual Tracking via Adversarial Learning",
          "Cross-Modal Deep Variational Hand Pose Estimation",
          "Light Field Blind Motion Deblurring",
          "End-To-End People Detection in Crowded Scenes",
          "Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling",
          "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume",
          "Rethinking the Inception Architecture for Computer Vision",
          "Spatially-Adaptive Filter Units for Deep Neural Networks",
          "Image Super-Resolution via Deep Recursive Residual Network",
          "Multiple Instance Detection Network With Online Instance Classifier Refinement",
          "Multiple People Tracking by Lifted Multicut and Person Re-Identification",
          "Scale-Recurrent Network for Deep Image Deblurring",
          "Tangent Convolutions for Dense Prediction in 3D",
          "Real-Time Seamless Single Shot 6D Object Pose Prediction",
          "Two-Stream Convolutional Networks for Dynamic Texture Synthesis",
          "Full Resolution Image Compression With Recurrent Neural Networks",
          "Lifting From the Deep: Convolutional 3D Pose Estimation From a Single Image",
          "A Closer Look at Spatiotemporal Convolutions for Action Recognition",
          "Face Normals \"In-The-Wild\" Using Fully Convolutional Networks",
          "Learning to Adapt Structured Output Space for Semantic Segmentation",
          "Learning Shape Abstractions by Assembling Volumetric Primitives",
          "MoCoGAN: Decomposing Motion and Content for Video Generation",
          "Learning Superpixels With Segmentation-Aware Affinity Loss",
          "Adversarial Discriminative Domain Adaptation",
          "Deep Image Prior",
          "Deep Feature Interpolation for Image Content Changes",
          "PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition",
          "Conditional Similarity Networks",
          "Neural Kinematic Networks for Unsupervised Motion Retargetting",
          "Show and Tell: A Neural Image Caption Generator",
          "Adversarial Feature Augmentation for Unsupervised Domain Adaptation",
          "Face Aging With Identity-Preserved Conditional Generative Adversarial Networks",
          "High-Resolution Image Synthesis and Semantic Manipulation With Conditional GANs",
          "Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition",
          "Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer",
          "Repulsion Loss: Detecting Pedestrians in a Crowd",
          "Revisiting Video Saliency: A Large-Scale Benchmark and a New Model",
          "SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation",
          "Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition",
          "Min-Entropy Latent Model for Weakly Supervised Object Detection",
          "Rethinking Feature Distribution for Loss Functions in Image Classification",
          "Bidirectional Retrieval Made Simple",
          "Convolutional Pose Machines",
          "Dense Human Body Correspondences Using Convolutional Networks",
          "Compressed Video Action Recognition",
          "Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning",
          "Quantized Convolutional Neural Networks for Mobile Devices",
          "Unsupervised Feature Learning via Non-Parametric Instance Discrimination",
          "Feature Generating Networks for Zero-Shot Learning",
          "Gibson Env: Real-World Perception for Embodied Agents",
          "Aggregated Residual Transformations for Deep Neural Networks",
          "Learning Descriptor Networks for 3D Shape Synthesis and Analysis",
          "Deep Texture Manifold for Ground Terrain Recognition",
          "AttnGAN: Fine-Grained Text to Image Generation With Attentional Generative Adversarial Networks",
          "Deep Image Matting",
          "Scene Graph Generation by Iterative Message Passing",
          "DenseASPP for Semantic Segmentation in Street Scenes",
          "Learning Face Age Progression: A Pyramid Architecture of GANs",
          "Neural Aggregation Network for Video Face Recognition",
          "Object Contour Detection With a Fully Convolutional Encoder-Decoder Network",
          "Robust Classification With Convolutional Prototype Learning",
          "Semantic Filtering",
          "GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose",
          "Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning",
          "Dilated Residual Networks",
          "Generative Image Inpainting With Contextual Attention",
          "MAttNet: Modular Attention Network for Referring Expression Comprehension",
          "PU-Net: Point Cloud Upsampling Network",
          "Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks",
          "Learning to Promote Saliency Detectors",
          "S3Pool: Pooling With Stochastic Spatial Sampling",
          "Adversarial Complementary Learning for Weakly Supervised Object Localization",
          "Deep Mutual Learning",
          "Densely Connected Pyramid Dehazing Network",
          "Density-Aware Single Image De-Raining Using a Multi-Stream Dense Network",
          "Grounding Referring Expressions in Images by Variational Context",
          "ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing",
          "Joint Pose and Expression Modeling for Facial Expression Recognition",
          "Learning a Deep Embedding Model for Zero-Shot Learning",
          "Residual Dense Network for Image Super-Resolution",
          "Single-Image Crowd Counting via Multi-Column Convolutional Neural Network",
          "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric",
          "Unsupervised Discovery of Object Landmarks as Structural Representations",
          "Visual Translation Embedding Network for Visual Relation Detection",
          "Ring Loss: Convex Feature Normalization for Face Recognition",
          "EAST: An Efficient and Accurate Scene Text Detector",
          "Learning Deep Features for Discriminative Localization",
          "Learning Rich Features for Image Manipulation Detection",
          "Weakly Supervised Instance Segmentation Using Class Peak Response",
          "Fast Training of Triplet-Based Deep Binary Embedding Networks",
          "Towards Effective Low-Bitwidth Convolutional Neural Networks",
          "Learning Spatial Regularization With Image-Level Supervisions for Multi-Label Image Classification",
          "Learning Transferable Architectures for Scalable Image Recognition",
          "LayoutNet: Reconstructing the 3D Room Layout From a Single RGB Image"
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('c50abdd9-f6da-4fe7-a77a-1e747fee1ead');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize(emb,meta,method='SpectralEmbedding',dim=2,  title='Combined Graph (Spectral Embedding)')\n",
    "visualize(emb,meta,method='PCA',dim=2,  title='Combined Graph (PCA)')"
   ]
  }
 ],
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