Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

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. Intuitively...More