With the increase in Generative Adversarial Networks (GAN) in recent years, there has been a lot of research on failure scenarios in GANs and how to train such models properly. While extremely powerful, GANs also prove to be unstable at times and suffer many issues. These range from convergence to the diversity of data generated. Most evaluation metrics are either unsuitable for certain use cases or rely on qualitative measures such as how "good" the results look to a human evaluator.
In 2018, On GANs and GMMs by Richardson et al. introduced a new metric to evaluate a GAN failure case known as mode collapse - when the model fails to generate diverse enough outputs. Such a metric allows for quantitative measurement of a GAN's performance.
This report will extend the paper by integrating this metric into DCGANs to evaluate their failure state on the CelebA dataset and visualize how the score changes during training.