Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models

10 Mar 2020Alessandro BerlatiOliver ScheelLuigi Di StefanoFederico Tombari

Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data, which is of special importance, as often multiple futures are equally likely... (read more)

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