Generating Unobserved Alternatives

Abstract

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. As a result, it can be used to generate high-quality outputs that are different from the observed output.

arXiv commentVideos in the article are also available as ancillary files in the previous version (arXiv:2011.01926v3). Website: https://niopeng.github.io/HyperRIM/

References in corpus (9)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Conditional Image Generation with PixelCNN Decoders
• The relativistic discriminator: a key element missing from standard GAN
• NVAE: A Deep Hierarchical Variational Autoencoder
• Learning in Implicit Generative Models
• Implicit Maximum Likelihood Estimation