https://github.com/williamfalcon/keras-deep-learning-paper-implementations
Keras implementation of Deep Learning papers
https://github.com/williamfalcon/keras-deep-learning-paper-implementations
Last synced: 8 months ago
JSON representation
Keras implementation of Deep Learning papers
- Host: GitHub
- URL: https://github.com/williamfalcon/keras-deep-learning-paper-implementations
- Owner: williamFalcon
- License: mit
- Created: 2017-03-27T00:40:26.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2018-07-25T21:41:28.000Z (almost 8 years ago)
- Last Synced: 2025-01-22T11:47:42.477Z (over 1 year ago)
- Size: 38.1 KB
- Stars: 95
- Watchers: 12
- Forks: 32
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Keras Deep Learning Paper Implementations
A curated list of implementations in keras.
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.
**We welcome your contributions!**
If you have any paper/code suggestions, please feel free to edit and sumbit a pull request.
---
### Imagenet Models
- 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)
- 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)
- 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)
- Resnet (2015), K. He et al. [[pdf]](https://arxiv.org/pdf/1512.03385.pdf) [[code]](https://github.com/raghakot/keras-resnet)
### Unsupervised / Generative Models
- 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)
- Deepmind's wavenet (2016), Van den Oord et al. [[pdf]](https://arxiv.org/pdf/1609.03499.pdf) [[code]](https://github.com/usernaamee/keras-wavenet)
- 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)
### Convolutional Models
- XCeption (2016), F. Chollet. [[pdf]](https://arxiv.org/pdf/1610.02357.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/xception.py)
- 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)
- 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)
- 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)
- DenseNet: Densely Connected Convolutional Network (2016), G. Huang. [[pdf]](https://arxiv.org/abs/1608.06993) [[code]](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet)
### LSTM
- 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)
- 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)
### Fun Models
- 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)
- 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)
- 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)
## Acknowledgement
Thank you for all your contributions.
## License
MIT
To the extent possible under law, [William Falcon](https://williamfalcon.com) has waived all copyright and related or neighboring rights to this work.