The Hidden Tasks of Generative Adversarial Networks: An Alternative Perspective on GAN Training

We present an alternative perspective on the training of generative adversarial networks (GANs), showing that the training step for a GAN generator decomposes into two implicit sub-problems. In the first, the discriminator provides new target data to the generator in the form of "inverse examples" produced by approximately inverting classifier labels. In the second, these examples are used as targets to update the generator via least-squares regression, regardless of the main loss specified to train the network. We experimentally validate our main theoretical result and demonstrate significant improvements over standard GAN training made possible by making these sub-problems explicit. We also introduce a simple representation of inductive bias in networks, which we apply to describing the generator's output relative to its regression targets.