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A family of super deep networks, referred to as residual networks or ResNet,achieved record-beating performance in various visual tasks such as imagerecognition, object detection, and semantic segmentation. The ability to trainvery deep networks naturally pushed the researchers to use enormous resourcesto achieve the best performance. Consequently, in many applications super deepresidual networks were employed for just a marginal improvement in performance.In this paper, we propose epsilon-ResNet that allows us to automaticallydiscard redundant layers, which produces responses that are smaller than athreshold epsilon, with a marginal or no loss in performance. Theepsilon-ResNet architecture can be achieved using a few additional rectifiedlinear units in the original ResNet. Our method does not use any additionalvariables nor numerous trials like other hyper-parameter optimizationtechniques. The layer selection is achieved using a single training process andthe evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNetdatasets. In some instances, we achieve about 80% reduction in the number ofparameters.

Learning Strict Identity Mappings in Deep Residual Networks