Generating Unobserved Alternatives with Tower Implicit Model (TIM)
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, TIM, that can generate multiple high quality predictions given the same input and achieves a reduction of the Fréchet Inception Distance (FID) by 19.6% on average compared to the baseline.
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