GAN Lab: Play with Generative Adversarial Networks in Your Browser!

Darker green means that samples in that region are more likely to be real; darker purple, more likely to be fake. As a GAN approaches the optimum, the whole heatmap will become more gray overall, signalling that the discriminator can no longer easily distinguish fake examples from the real ones. In a GAN, its two networks influence each other as they iteratively update themselves. A great use for GAN Lab is to use its visualization to learn how the generator incrementally updates to improve itself to generate fake samples that are increasingly more realistic. The generator does it by trying to fool the discriminator.