├── install_system_dependencies ├── configure_jupyter ├── requirements.txt ├── LICENSE ├── .gitignore └── README.md /install_system_dependencies: -------------------------------------------------------------------------------- 1 | # !/usr/bin/env bash 2 | sudo apt-get install libwebp-dev 3 | -------------------------------------------------------------------------------- /configure_jupyter: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | JUPYTER_DATA_DIR=$(jupyter --data-dir) 4 | 5 | jt -t gruvboxd 6 | jupyter contrib nbextension install --user 7 | jupyter nbextensions_configurator enable --user 8 | 9 | # install vim binding extension 10 | mkdir -p $JUPYTER_DATA_DIR/nbextensions 11 | cd $JUPYTER_DATA_DIR/nbextensions &&\ 12 | git clone https://github.com/lambdalisue/jupyter-vim-binding vim_binding &&\ 13 | chmod -R go-w vim_binding 14 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # pytorch 2 | http://download.pytorch.org/whl/cu80/torch-0.2.0.post3-cp35-cp35m-manylinux1_x86_64.whl 3 | torchvision==0.1.8 4 | torchtext==0.1.1 5 | visdom 6 | 7 | # tensorflow 8 | tensorflow-gpu 9 | prettytensor 10 | 11 | # data 12 | scipy 13 | scikit-learn 14 | numpy 15 | pillow 16 | 17 | # utils (debugging) 18 | pdbpp 19 | ipdb 20 | ipython 21 | jupyter 22 | jupyterthemes 23 | jupyter_contrib_nbextensions 24 | jupyter_nbextensions_configurator 25 | 26 | # utils (others) 27 | colorama 28 | tqdm 29 | lmdb 30 | requests 31 | fake-useragent 32 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Ha Junsoo 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | *.ipynb 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # dotenv 80 | .env 81 | 82 | # virtualenv 83 | venv/ 84 | ENV/ 85 | 86 | # Spyder project settings 87 | .spyderproject 88 | 89 | # Rope project settings 90 | .ropeproject 91 | 92 | # vim 93 | *.sw* 94 | 95 | checkpoints 96 | .DS_Store 97 | 98 | # pytorch dataset 99 | processed 100 | raw 101 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Papers 2 | - 💡 Papers which gave me insights 3 | - 📓 Categorized by problems and idea 4 | - 📆 Sorted by chronological order 5 | - 🔨 Clean implementations 6 | 7 | 8 | 9 | ## Model Architecture 10 | 11 | ### CNN 12 | - *Striving for Simplicity: The All Convolutional Net* [[arxiv, 14.12]](http://arxiv.org/abs/1412.6806) 13 | - *U-Net: Convolutional Networks for Biomedical Image Segmentation* [[arxiv, 15.05]](https://arxiv.org/abs/1505.04597) 14 | - *Deep Residual Learning for Image Recognition* [[arxiv, 15.12]](https://arxiv.org/abs/1512.03385) 15 | - *Wide Residual Networks* [[arxiv, 16.05]](https://arxiv.org/abs/1605.07146) [[**PyTorch**]](https://github.com/kuc2477/pytorch-wrn) 16 | 17 | 18 | 19 | ## Distribution Learning 20 | 21 | ### RNN 22 | - *Generating Sequences With Recurrent Neural Networks* [[arxiv, 13.08]](http://arxiv.org/abs/1308.0850) 23 | 24 | ### GAN 25 | 26 | #### Theories 27 | - *Generative Adversarial Networks* [[arxiv, 14.06]](http://arxiv.org/abs/1406.2661) 28 | - *Energy-based Generative Adversarial Network* [[arxiv, 16.09]](https://arxiv.org/abs/1609.03126) 29 | - *Wasserstein GAN* [[arxiv, 17.01]](http://arxiv.org/abs/1701.07875) [[**TensorFlow**]](https://github.com/kuc2477/tensorflow-wgan) 30 | - *Boundary-Seeking Generative Adversarial Networks* [[arxiv, 17.02]](http://arxiv.org/abs/1702.08431) 31 | - *BEGAN: Boundary Equilibrium Generative Adversarial Networks* [[arxiv, 17.03]](https://arxiv.org/abs/1703.10717) 32 | - *Improved Training of Wasserstein GANs* [[arxiv, 17.04]](https://arxiv.org/abs/1704.00028) [[**PyTorch**]](https://github.com/kuc2477/pytorch-wgan-gp) 33 | 34 | #### Coarse-to-Fine Architectures 35 | - *Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks* [[arxiv, 15.06]](https://arxiv.org/abs/1506.05751) 36 | - *Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks* [[arxiv, 15.11]](http://arxiv.org/abs/1511.06434) [[**TensorFlow**]](https://github.com/kuc2477/tensorflow-dcgan) 37 | - *StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks* [[arxiv, 16.12]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_StackGAN_Text_to_ICCV_2017_paper.pdf) 38 | - *StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks* [[arxiv, 17.10]](https://arxiv.org/abs/1710.10916) 39 | - *Progressive Growing of GANs for Improved Quality, Stability, and Variation* [[arxiv, 17.10]](https://arxiv.org/abs/1710.10196) 40 | - *High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs* [[arxiv, 17.11]](https://arxiv.org/abs/1711.11585) 41 | 42 | #### Learning Conditional Distributions 43 | - *Conditional Generative Adversarial Nets* [[arxiv, 14.11]](https://arxiv.org/abs/1411.1784) 44 | - *InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets* [[arxiv, 16.06]](https://arxiv.org/abs/1606.03657) [[**TensorFlow**]](https://github.com/kuc2477/tensorflow-infogan) 45 | - *Generative Adversarial Text to Image Synthesis* [[arxiv, 16.05]](https://arxiv.org/abs/1605.05396) 46 | - *Adversarilly Learned Inference* [[arxiv, 16.06]](https://arxiv.org/abs/1606.00704) 47 | - *Learning What and Where to Draw* [[arxiv, 16.10]](https://arxiv.org/abs/1610.02454) 48 | - *Image-to-Image Translation with Conditional Adversarial Networks* [[arxiv, 16.11]](https://arxiv.org/abs/1611.07004) 49 | - *Triple Generative Adversarial Nets* [[arxiv, 17.03]](https://arxiv.org/abs/1703.02291) 50 | - *Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks* [[arxiv, 17.03]](https://arxiv.org/abs/1703.10593) 51 | - *Learning to Discover Cross-Domain Relations with Generative Adversarial Networks* [[arxiv, 17.03]](http://arxiv.org/abs/1703.05192) 52 | 53 | #### Avoiding Mode Collapse to Learn Full Manifolds 54 | - *Unrolled Generative Adversarial Networks* [[arxiv, 16.11]](https://arxiv.org/abs/1611.02163) 55 | - *Global versus Localized Generative Adversarial Nets* [[arxiv, 17.11]](https://arxiv.org/abs/1711.06020) 56 | 57 | 58 | ### VAE 59 | - *Auto-Encoding Variational Bayes* [[arxiv, 13.12]](http://arxiv.org/abs/1312.6114) [[**PyTorch**]](https://github.com/kuc2477/pytorch-vae) 60 | 61 | 62 | 63 | ## Representation Learning 64 | - *The Consciousness Prior* [[arxiv, 17.09]](https://arxiv.org/abs/1709.08568) 65 | 66 | 67 | 68 | ## Memory 69 | - *Neural Turing Machines* [[arxiv, 14.10]](http://arxiv.org/abs/1410.5401) [[**PyTorch**]](https://github.com/kuc2477/pytorch-ntm) 70 | - *Memory Networks* [[arxiv, 14.10]](https://arxiv.org/abs/1410.3916) 71 | - *End-To-End Memory Networks* [[arxiv, 15.03]](https://arxiv.org/abs/1503.08895) [[**PyTorch**]](https://github.com/kuc2477/pytorch-memn2n) 72 | - *Hybrid computing using a neural network with dynamic external memory* [[Nature, 16.10]](https://www.nature.com/articles/nature20101.epdf?author_access_token=ImTXBI8aWbYxYQ51Plys8NRgN0jAjWel9jnR3ZoTv0MggmpDmwljGswxVdeocYSurJ3hxupzWuRNeGvvXnoO8o4jTJcnAyhGuZzXJ1GEaD-Z7E6X_a9R-xqJ9TfJWBqz) 73 | 74 | 75 | 76 | ## Continual Learning 77 | - *Overcoming catastrophic forgetting in neural networks* [[arxiv, 16.12]](https://arxiv.org/abs/1612.00796) [[**PyTorch**]](https://github.com/kuc2477/pytorch-ewc) 78 | - *Continual Learning with Deep Generative Replay* [[arxiv, 17.05]](https://arxiv.org/abs/1705.08690) [[**PyTorch**]](https://github.com/kuc2477/pytorch-deep-generative-replay) 79 | 80 | 81 | 82 | ## Attention 83 | - *Neural Machine Translation by Jointly Learning to Align and Translate* [[arxiv, 14.09]](http://arxiv.org/abs/1409.0473) 84 | - *Show, Attend and Tell: Neural Image Caption Generation with Visual Attention* [[arxiv, 15.02]](http://arxiv.org/abs/1502.03044) 85 | 86 | 87 | 88 | ## Relational Reasoning 89 | - *A simple neural network module for relational reasoning* [[arxiv, 17.06]](https://arxiv.org/abs/1706.01427) 90 | 91 | 92 | 93 | ## Learning Equivariance 94 | - *Dynamic Routing Between Capsules* [[arxiv, 17.10]](https://arxiv.org/abs/1710.09829) 95 | 96 | 97 | 98 | ## Learning Invariance 99 | - *Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition* [[arxiv, 14.06]](https://arxiv.org/abs/1406.4729) 100 | - *Spatial Transformer Networks* [[arxiv, 15.06]](http://arxiv.org/abs/1506.02025) 101 | - *Deformable Convolutional Networks* [[arxiv, 17.03]](https://arxiv.org/abs/1703.06211) 102 | 103 | 104 | 105 | ## Learning Optimization 106 | - *Maxout Networks* [[arxiv, 13.02]](https://arxiv.org/abs/1302.4389) 107 | 108 | 109 | 110 | ## Model Optimization 111 | - *SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization* [[ICML, 17.07]](http://proceedings.mlr.press/v70/kim17b/kim17b.pdf) [[**PyTorch**]](https://github.com/kuc2477/pytorch-splitnet) 112 | 113 | 114 | 115 | ## Network Visualization 116 | - *Visualizing and Understanding Convolutional Networks* [[arxiv, 13.11]](http://arxiv.org/abs/1311.2901) 117 | - *Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps* [[arxiv, 13.12]](http://arxiv.org/abs/1312.6034) 118 | - *Understanding Deep Image Representations by Inverting Them* [[arxiv, 14.12]](http://arxiv.org/abs/1412.0035) 119 | 120 | 121 | 122 | ## Applications 123 | 124 | ### Object Detection 125 | - *Rich feature hierarchies for accurate object detection and semantic segmentation* [[arxiv, 13.11]](https://arxiv.org/abs/1311.2524) 126 | - *Fast R-CNN* [[arxiv, 15.04]](https://arxiv.org/abs/1504.08083) 127 | - *Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks* [[arxiv, 15.06]](https://arxiv.org/abs/1506.01497) 128 | - *You Only Look Once: Unified, Real-Time Object Detection* [[arxiv, 15.06]](https://arxiv.org/abs/1506.02640) 129 | - *SSD: Single Shot MultiBox Detector* [[arxiv, 15.12]](https://arxiv.org/abs/1512.02325) 130 | - *YOLO9000: Better, Faster, Stronger* [[arxiv, 16.12]](https://arxiv.org/abs/1612.08242) 131 | - *Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video* [[arxiv, 17.09]](https://arxiv.org/abs/1709.05943) 132 | 133 | ### Semantic Segmentation 134 | - *Learning Deconvolution Network for Semantic Segmentation* [[arxiv, 15.05]](https://arxiv.org/abs/1505.04366) 135 | - *Fully Convolutional Networks for Semantic Segmentation* [[arxiv, 16.05]](https://arxiv.org/abs/1605.06211) 136 | 137 | ### Visual QA 138 | - *TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering* [[arxiv, 17.04]](https://arxiv.org/abs/1704.04497) 139 | - *A Read-Write Memory Network for Movie Story Understanding* [[arxiv, 17.09]](https://arxiv.org/abs/1709.09345) 140 | --------------------------------------------------------------------------------