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In this work we introduce a new framework for performing temporal predictionsin the presence of uncertainty. It is based on a simple idea of disentanglingcomponents of the future state which are predictable from those which areinherently unpredictable, and encoding the unpredictable components into alow-dimensional latent variable which is fed into a forward model. Our methoduses a supervised training objective which is fast and easy to train. Weevaluate it in the context of video prediction on multiple datasets and showthat it is able to consistently generate diverse predictions without the needfor alternating minimization over a latent space or adversarial training.

Prediction Under Uncertainty with Error-Encoding Networks