Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
Standard GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to stable implementations of GAN architectures (i.e. ACGan, InfoGAN, Improved wGAN) and other promising variations of GANs (i.e. GAN hacks, local adversarial loss, etc...).
masterbranch serves as a simple, clean and robust starting point for GAN R&D. Contributions are encouraged in the form of new branches and/or improvements to
master. Ideally branches will follow
master'scoding style and deviate as little (realistically) as possible from it.
master: Standard GAN.
ac-gan: Auxiliary classifier GAN as described in: Conditional image synthesis with auxiliary classifier GANs.
info-gan: Information maximizing GAN as described in: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.
cGAN: As described in: Image-to-Image Translation with Conditional Adversarial Networks.
wGAN: As described in: Wasserstein GAN with improvements as described in: Improved Training of Wasserstein GANs.
ACGANis a more limited form of
InfoGANcan take an arbitrary number of categorical and continuous latent variables as input to the generator. ACGAN is an InfoGAN in the case where the generator takes one categorical latent variable as input corresponding to the label of the image to be generated.
wGANobjective function should be used for all variations of GANs instead of the Jenson-Shannon divergence.
This repo and its branches were derived from Waya.ai's code base and are released in a cleaner and more modular form. I haven't fully tested each branch yet though so there may be some issues, and the GANs may need to be tuned a bit to converge properly.
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