VEEGAN - Reducing Mode Collapse in GANs Using Implicit Variational Learning

Explore the innovative VEEGAN approach to addressing mode collapse in Generative Adversarial Networks (GANs) through implicit variational learning. Delve into the fundamentals of GANs and the challenges posed by mode collapse before examining how VEEGAN effectively tackles this issue. Gain insights into the reconstructor's functionality, the process of approximately inverting the generator Gyl, and the intricacies of the Reconstructor Network Objective Function. Analyze the results of VEEGAN's application to synthetic datasets, stacked MNIST, and CIFAR, understanding its impact on improving GAN performance and diversity in generated samples.

What are Generative Adversarial Networks
What is mode collapse
How does VEEGAN address mode collapse?
How the reconstructor works
Approximately invert the generator Gyl
Reconstructor Network Objective Function
Results for the Synthetic datasets
Results for stacked MNIST
Stacked MNIST and CIFAR