On the Units of GANs (Extended Abstract)

Understand

Generative Adversarial Networks (GANs) have achieved impressive results for many real-world applications.

• As an active research topic, many GAN variants have emerged with improvements in sample quality and training stability.
• However, visualization and understanding of GANs is largely missing.
• How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models.