Hi, this repository was created as a student of the master's degree in (computer engineering? i'm not sure that is the correct translation :P), and has the propose to help me understend the topic "generative adversial network". Also the english isn´t my strong point, so i hope i can improve as well.
Here i present several GAN models and basic classifiers (just to compare results) in format of notebook implemented with tensorflow using the layers API
- GAN (original 2014)
- Conditional GAN
- NN for MNIST Classification (simple nn, no dropout or other fancies tecniques, just to have baseline score)
- Auxiliar classifier GAN
- CNN for MNIST Classification (just to have baseline score)
- Deep Convolution GAN
- Condition Deep Convolution GAN
- Condition Deep Convolution GAN
- Semi Supervised GAN (Improved Techniques for Training GANs)
- Information Retrieval GAN
- GAN - https://arxiv.org/abs/1406.2661
- Conditional GAN - https://arxiv.org/abs/1411.1784
- Auxiliar classifier GAN - https://arxiv.org/abs/1610.09585
- Deep Convolution GAN - https://arxiv.org/abs/1511.06434
- SSGAN - https://arxiv.org/abs/1606.03498
- Book - Hands-On Machine Learning with Scikit-Learn and TensorFlow (first steps)
- Book code - https://github.com/ageron/handson-ml
- Models code examples - https://github.com/wiseodd/generative-models
- ICGAN - https://arxiv.org/abs/1611.06355
- EBGAN - https://arxiv.org/abs/1609.03126
- BEGAN - https://blog.heuritech.com/2017/04/11/began-state-of-the-art-generation-of-faces-with-generative-adversarial-networks/
- BEGAN - (paper)
- IRGAN - (paper)
- jupyter notebook
- tensorflow (1.4, 1.5 (soon)) (All the code was run on GPU version (but CPU should work to))
- numpy
- matplotlib