On the "steerability" of generative adversarial networks

Understand

An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab.

• This has been attributed to biased training data, which provide poor coverage over real world events.
• Generative models are no exception, but recent advances in generative adversarial networks (GANs) suggest otherwise - these models can now synthesize strikingly realistic and diverse images.
• Is generative modeling of photos a solved problem? We show that although current GANs can fit standard datasets very well, they still fall short of being comprehensive models of the visual manifold.