• Conditional version of generative adversarial nets
  • In an unconditioned generative model, there is no control on modes of the data being generated.
  • In the Conditional GAN (CGAN), the generator learns to generate a fake sample with a specific condition or characteristics (such as a label associated with an image or more detailed tag) rather than a generic sample from unknown noise distribution.
  • Simply feeding the data y, and conditioning both the generator and discriminator
• Adversarial learning loss of CGAN

• Generator
  • Input: z (100 dimension), Output: generated image
  • FC [256, 512, 1024]
• Discriminator
  • Input: image (generated or real), Output: fake/real
  • FC [1024, 512, 256]
• Adversarial learning: refer to generator_train_step(), discriminator_train_step()

https://www.kaggle.com/datasets/zalando-research/fashionmnist

https://www.kaggle.com/code/arturlacerda/pytorch-conditional-gan/notebook

@article{CGAN,
  title={Conditional Generative Adversarial Nets},
  author={Mehdi Mirza, Simon Osindero},
  journal = {arXiv},
  year={2014}
}