TF-GAN

Components like core, features, losses, and evaluation are designed to exist independently, allowing developers to pick and choose what they need and integrate with custom code, as outlined in the library structure.

Includes advanced losses such as Wasserstein loss, gradient penalty, and mutual information penalty, which are crucial for stable GAN training and documented in the losses module.

Implements metrics like Inception Score, Fréchet Distance, and Kernel Distance using pretrained networks, ensuring consistent and comparable results across experiments, as highlighted in the evaluation section.

Provides simple and state-of-the-art examples, including those used in published Google papers, offering practical guidance for various GAN setups and reducing implementation errors.