All experiments were performed using a single Tesla V100 GPU. We use these trained networks and treat them as pre-trained models, i.e. we consider the IC-only" setup, where we do not change the base network. For CIFAR-10 and CIFAR-100 we train ICs for 50 epochs using the Adam optimizer with learning rate set to 0.001, but lowered by a factor of 10 after 15 epochs. When training on Tiny ImageNet, the learning rate is additionally lowered again by the same factor after epoch 40. On ImageNet (on the pretrained ResNet-50 from the torchvision package), the ICs are trained for 40epochs, with the initial learning rate of 0.00001 being reduced by a factor of 10 in epochs 20 and 30.

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of a neural network. ICs can quickly return predictions for easy examples and, as a result, reduce the average inference time of the whole model. However, if a particular IC does not decide to return an answer early, its predictions are discarded, with its computations effectively being wasted. To solve this issue, we introduce Zero Time Waste (ZTW), a novel approach in which each IC reuses predictions returned by its predecessors by (1) adding direct connections between ICs and (2) combining previous outputs in an ensemble-like manner. We conduct extensive experiments across various datasets and architectures to demonstrate that ZTW achieves a significantly better accuracy vs. inference time trade-off than other recently proposed early exit methods.

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We thank the reviewers for feedback, as well as efforts in reviewing. We respond to each comment below. Overall, there is no significant contribution to unsupervised pre-training. " The fact that our main contribution is a detailed procedure, rather than a theorem, architecture, or other artifact, We believe our contributions are significant. Indeed, R3 recognizes that "the simple semi-supervised framework is still I think it will inspire several future works." " While we believe ImageNet is a much more These results can be further improved with better augmentations during fine-tuning and an extra distillation step.