[Web]

Provides the exact code and hyperparameters from the seminal 2014 GAN paper, enabling precise replication of original experiments as emphasized in the README.

Includes parzen_ll.py for model evaluation using the Parzen density technique, a key method detailed in the README for assessing generative performance.

Offers separate YAML files for datasets like MNIST and CIFAR-10, simplifying training on the same data used in the original research as noted in the README.

Serves as a canonical implementation for studying the evolution of generative adversarial networks, with direct ties to the foundational paper.