Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

22 Aug 2017Hanzhang HuDebadeepta DeyMartial HebertJ. Andrew Bagnell

This work considers the trade-off between accuracy and test-time computational cost of deep neural networks (DNNs) via \emph{anytime} predictions from auxiliary predictions. Specifically, we optimize auxiliary losses jointly in an \emph{adaptive} weighted sum, where the weights are inversely proportional to average of each loss... (read more)

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