Structured correlations in data can boost learning of weak features in GANs, but excessive correlation destabilizes training—there's a sweet spot determined by learning rates and noise.
This paper analyzes GAN training mathematically by studying how a linear generator learns data structure. The key innovation is handling realistic data with correlated features and class-dependent patterns—not just simple diagonal structure. The authors prove training converges to predictable equations and show that smart use of correlations can help weak features become learnable.