Title: Diminishing Non-important Gradients for Training Dynamic Early-Exiting Networks
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Early-exiting; gradient conflict

Abstract: Early-exiting is an effective mechanism to improve computation efficiency. By adding classifiers to intermediate layers of deep learning networks, early exiting networks can terminate the inference early for easy samples, thus reducing the average inference time. Gradient conflicts between different classifiers are a key challenge in training early-exiting networks. However, current state-of-the-art methods focus solely on the trade-off between gradients, without evaluating whether these gradients are actually necessary. To mitigate this issue, we propose a novel adaptive damping training strategy that adaptively diminishes non-important gradients during the training process based on data samples and classifiers. By adding a damping neuron to the last fully connected layer of each classifier and using our proposed damping loss, our approach effectively reduces gradients that are unlikely to be beneficial. Moreover, we propose power-sqrt loss to concentrate the gradients of damping neurons on classifiers that exhibit relatively better training performance. Experiments on CIFAR and ImageNet demonstrate our proposed method gains significant accuracy improvement for all classifiers with negligible computation increases.