Ambiguity in Recurrent Models: Predicting Multiple Hypotheses with Recurrent Neural Networks

Multiple Hypothesis Prediction (MHP) models have been introduced to deal with uncertainty in feedforward neural networks, in particular it has been shown how to easily convert a standard single-prediction neural network into one able to show many feasible outcomes. Ambiguity, however, is present also in problems where feedback model are needed, such as sequence generation and time series classification. In our work, we propose an extension of MHP to Recurrent Neural Networks (RNNs), especially those consisting of Long Short-Term Memory units. We test the resulting models on both regression and classification problems using public datasets, showing promising results. Our way to build MHP models can be used to retrofit other works, leading the way towards further research. We can find many possible application scenarios in the autonomous driv- ing environment. For example, trajectory prediction, for humans and cars, or intention classification (e.g. lane change detection) are both tasks where ambiguous situations are frequent.