Prediction Under Uncertainty with Error Encoding Networks
In this work we introduce a new framework for performing temporal predictionsin the presence of uncertainty.It is based on a simple idea of disentangling com-ponents of the future state which are predictable from those which are inherentlyunpredictable, and encoding the unpredictable components into a low-dimensionallatent variable which is fed into the forward model.Our method uses a simple su-pervised training objective which is fast and easy to train.We evaluate it in thecontext of video prediction on multiple datasets and show that it is able to consi-tently generate diverse predictions without the need for alternating minimizationover a latent space or adversarial training.
A simple and easy to train method for multimodal prediction in time series. This paper introduces a times-series prediction model that that learns a deterministic mapping and trains another net to predict future frames given the input and residual error from the first network.The paper proposes a model for prediction under uncertainty where they separate out deterministic component prediction and uncertain component prediction.