Why Spectral Normalization Stabilizes GANs: Analysis and Improvements


Figure 1: Training instability is one of the biggest challenges in training GANs. Despite the existence of successful heuristics like Spectral Normalization (SN) for improving stability, it is poorly-understood why they work. In our research, we theoretically explain why SN stabilizes GAN training. Using these insights, we further propose a better normalization technique for improving GANs' stability called Bidirectional Scaled Spectral Normalization. Generative adversarial networks (GANs) are a class of popular generative models enabling many cutting-edge applications such as photorealistic image synthesis.

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