Generating Unobserved Alternatives

Understand

We consider problems where multiple predictions can be considered correct, but only one of them is given as supervision.

• This setting differs from both the regression and class-conditional generative modelling settings: in the former, there is a unique observed output for each input, which is provided as supervision; in the latter, there are many observed outputs for each input, and many are provided as supervision.
• Applying either regression methods and conditional generative models to the present setting often results in a model that can only make a single prediction for each input.
• We explore several problems that have this property and develop an approach that can generate multiple high-quality predictions given the same input.