TODO

[lemairecarl]

Convolutional nets

Classification / Feature extractors

• [x] LeNet (Gradient-Based Learning Applied to Document Recognition)
  http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
• [x] AlexNet (ImageNet Classification with Deep Convolutional Neural Networks) http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
• [x] GoogLeNet, Inception (Going Deeper with Convolutions)
  https://arxiv.org/abs/1409.4842
• [x] VGGNet
• [x] Inception v2/v3 (Rethinking the Inception Architecture for Computer Vision) https://arxiv.org/abs/1512.00567
• [x] ResNet
• [ ] Identity Mappings in Deep Residual Networks https://arxiv.org/abs/1603.05027
• [x] Xception: Deep Learning with Depthwise Separable Convolutions https://arxiv.org/abs/1610.02357
• [x] DenseNet (Densely Connected Convolutional Networks)
  https://arxiv.org/abs/1608.06993
• [x] DelugeNets: Deep Networks with Massive and Flexible Cross-layer Information Inflows https://arxiv.org/abs/1611.05552
• [ ] Deeply-Supervised Nets
  https://arxiv.org/abs/1409.5185
• [x] Striving for Simplicity: The All Convolutional Net (All-CNN) https://arxiv.org/abs/1412.6806
• [x] SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size https://arxiv.org/abs/1602.07360

Detection, Localization, Segmentation

• [x] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
  https://arxiv.org/abs/1312.6229
• [x] FCNN: Fully Convolutional Networks for Semantic Segmentation https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf https://computing.ece.vt.edu/~f15ece6504/slides/L13_FCN.pdf
• [x] Siamese Net : Learning a Similarity Metric Discriminatively, with Application to Face Verification http://yann.lecun.com/exdb/publis/pdf/chopra-05.pdf
• [x] ConvDeconv : Learning Deconvolution Network for Semantic Segmentation http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Noh_Learning_Deconvolution_Network_ICCV_2015_paper.pdf
• [x] R-CNN (Rich feature hierarchies for accurate object detection and semantic segmentation) https://arxiv.org/pdf/1311.2524.pdf
• [x] Fast R-CNN
  https://arxiv.org/abs/1504.08083
• [x] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  https://arxiv.org/abs/1506.01497
• [x] The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation https://arxiv.org/pdf/1611.09326.pdf
• [x] YOLO
• [x] SSD
• [x] Feature Pyramid Network for Object Detection https://arxiv.org/pdf/1612.03144.pdf
• [x] Network In Network --- 1x1 conv https://arxiv.org/abs/1312.4400
• [x] DeepMask https://arxiv.org/pdf/1506.06204.pdf
• [x] SharpMask https://arxiv.org/pdf/1603.08695.pdf
• [x] The Importance of Skip Connections in Biomedical Image Segmentation

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Fundamental research / Network design

• [x] Neural Architecture Search with Reinforcement Learning https://arxiv.org/abs/1611.01578
• [ ] Visualizing and Understanding Convolutional Networks https://arxiv.org/abs/1311.2901
• [ ] Intriguing properties of neural networks (fooling images)
  https://arxiv.org/abs/1312.6199
• [ ] Explaining and Harnessing Adversarial Examples
  https://arxiv.org/abs/1412.6572
• [ ] Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
  https://arxiv.org/abs/1312.6034
• [ ] Understanding Neural Networks Through Deep Visualization https://arxiv.org/abs/1506.06579
• [x] Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626
• [ ] Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding https://arxiv.org/abs/1510.00149
• [ ] DSD: Dense-Sparse-Dense Training for Deep Neural Networks https://arxiv.org/abs/1607.04381
• [ ] Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
  https://arxiv.org/abs/1506.02142

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Semi-supervised Learning

• [ ] Semi-Supervised Learning with Ladder Networks https://arxiv.org/abs/1507.02672
• [ ] Deconstructing the Ladder Network Architecture
  https://arxiv.org/abs/1511.06430

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Approximate Nearest Neighbor

• [ ] Scalable Nearest Neighbor Algorithms for High Dimensional Data http://www.cs.ubc.ca/research/flann/uploads/FLANN/flann_pami2014.pdf
• [ ] Fast Matching of Binary Features http://www.cs.ubc.ca/research/flann/uploads/FLANN/binary_matching_crv2012.pdf
• [ ] Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration http://www.cs.ubc.ca/research/flann/uploads/FLANN/flann_visapp09.pdf

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Other

• [ ] Practical Recommendations for Gradient-Based Training of Deep Architectures
  https://arxiv.org/pdf/1206.5533.pdf
• [x] A Few Useful Things to Know about Machine Learning http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
• [ ] Learning Multiple Layers of Features from Tiny Images http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
• [ ] t-SNE http://lvdmaaten.github.io/tsne

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Lecture/course notes

• [x] Support Vector Machines (CS229 Lecture notes) https://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf

[lemairecarl]

@jodoin Please edit the list above to remove stuff that you don't think should be on the site.

[jodoin]

They all disserve to be on the site. They might not all be "essentials" however.

[Dref360]

I'll take Siamese since I already worked with those

[czotti]

I take the ConvDeconv!