├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 William Falcon 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Keras Deep Learning Paper Implementations 2 | 3 | A curated list of implementations in keras. 4 | 5 | It's a bit of a hassle to find implementation of most of the latest papers. Hopefully this allows anyone to get up and running with the state-of-the-art networks in little to no time. 6 | 7 | **We welcome your contributions!** 8 | 9 | If you have any paper/code suggestions, please feel free to edit and sumbit a pull request. 10 | 11 | --- 12 | 13 | ### Imagenet Models 14 | - Alexnet (2012), A. Krizhevsky et al. [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) [[code]](https://gist.github.com/JBed/c2fb3ce8ed299f197eff) 15 | - VGG16 (2014), K. Simonyan et al. [[pdf]](https://arxiv.org/pdf/1409.1556.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/vgg16.py) 16 | - VGG19 (2014), K. Simonyan et al. [[pdf]](https://arxiv.org/pdf/1409.1556.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/vgg19.py) 17 | - Resnet (2015), K. He et al. [[pdf]](https://arxiv.org/pdf/1512.03385.pdf) [[code]](https://github.com/raghakot/keras-resnet) 18 | 19 | 20 | ### Unsupervised / Generative Models 21 | 22 | - Pix2Pix. Image-to-Image Translation with Conditional Adversarial Networks (2016), P. Isola et al. [[pdf]](https://arxiv.org/pdf/1611.07004.pdf) [[code]](https://github.com/williamFalcon/pix2pix-keras) 23 | - Deepmind's wavenet (2016), Van den Oord et al. [[pdf]](https://arxiv.org/pdf/1609.03499.pdf) [[code]](https://github.com/usernaamee/keras-wavenet) 24 | - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. (2016), C. Ledig et al. [[pdf]](https://arxiv.org/pdf/1609.04802v2.pdf) [[code]](https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks) 25 | 26 | ### Convolutional Models 27 | - XCeption (2016), F. Chollet. [[pdf]](https://arxiv.org/pdf/1610.02357.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/xception.py) 28 | - Inception v3 (2015), C. Szegedy et al. [[pdf]](https://arxiv.org/pdf/1512.00567.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/inception_v3.py) 29 | - Image Super-Resolution Using Deep Convolutional Networks (2015), C. Dong et al. [[pdf]](https://arxiv.org/pdf/1501.00092v3.pdf) [[code]](https://github.com/titu1994/Image-Super-Resolution) 30 | - Time-series modeling with undecimated fully convolutional neural networks (2015), R. Mittelman. [[pdf]](https://arxiv.org/pdf/1508.00317.pdf) [[code]](https://github.com/lukovkin/ufcnn-keras) 31 | - DenseNet: Densely Connected Convolutional Network (2016), G. Huang. [[pdf]](https://arxiv.org/abs/1608.06993) [[code]](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet) 32 | 33 | ### LSTM 34 | - Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (2016), D. Neil [[pdf]](https://arxiv.org/pdf/1610.09513.pdf) [[code]](https://github.com/fferroni/PhasedLSTM-Keras) 35 | - Bidirectional LSTM: Neural Architectures for Named Entity Recognition (2016), G. Lample [[pdf]](http://www.aclweb.org/anthology/N16-1030) [[code]](https://gist.github.com/dirko/1d596ca757a541da96ac3caa6f291229) 36 | 37 | ### Fun Models 38 | 39 | - Deep Dream. Inceptionism: Going Deeper into Neural Networks (2015), A. Mordvintsev et al. at Google. [[blog]](https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html) [[code]](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py) 40 | - Style Transfer. Image Style Transfer Using Convolutional Neural Networks (2016), L. Gatys. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) [[code]](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py) 41 | - Fast (realtime) Neural Style Transfer. Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016), J. Johnson. [[pdf]](https://arxiv.org/pdf/1603.08155.pdf) [[code]](https://github.com/titu1994/Fast-Neural-Style) 42 | 43 | ## Acknowledgement 44 | 45 | Thank you for all your contributions. 46 | 47 | ## License 48 | MIT 49 | 50 | To the extent possible under law, [William Falcon](https://williamfalcon.com) has waived all copyright and related or neighboring rights to this work. 51 | --------------------------------------------------------------------------------