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.
- Alexnet (2012), A. Krizhevsky et al. [pdf] [code]
- VGG16 (2014), K. Simonyan et al. [pdf] [code]
- VGG19 (2014), K. Simonyan et al. [pdf] [code]
- Resnet (2015), K. He et al. [pdf] [code]
Unsupervised / Generative Models
- Pix2Pix. Image-to-Image Translation with Conditional Adversarial Networks (2016), P. Isola et al. [pdf] [code]
- Deepmind's wavenet (2016), Van den Oord et al. [pdf] [code]
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. (2016), C. Ledig et al. [pdf] [code]
- XCeption (2016), F. Chollet. [pdf] [code]
- Inception v3 (2015), C. Szegedy et al. [pdf] [code]
- Image Super-Resolution Using Deep Convolutional Networks (2015), C. Dong et al. [pdf] [code]
- Time-series modeling with undecimated fully convolutional neural networks (2015), R. Mittelman. [pdf] [code]
- DenseNet: Densely Connected Convolutional Network (2016), G. Huang. [pdf] [code]
- Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (2016), D. Neil [pdf] [code]
- Bidirectional LSTM: Neural Architectures for Named Entity Recognition (2016), G. Lample [pdf] [code]
- Deep Dream. Inceptionism: Going Deeper into Neural Networks (2015), A. Mordvintsev et al. at Google. [blog] [code]
- Style Transfer. Image Style Transfer Using Convolutional Neural Networks (2016), L. Gatys. [pdf] [code]
- Fast (realtime) Neural Style Transfer. Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016), J. Johnson. [pdf] [code]
Thank you for all your contributions.
To the extent possible under law, William Falcon has waived all copyright and related or neighboring rights to this work.