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The challenge of speeding up deep learning models during the deployment phasehas been a large, expensive bottleneck in the modern tech industry. In thispaper, we examine the use of both regularization and pruning for reducedcomputational complexity and more efficient inference in Deep Neural Networks(DNNs). In particular, we apply mixup and cutout regularizations and softfilter pruning to the ResNet architecture, focusing on minimizingfloating-point operations (FLOPs). Furthermore, by using regularization inconjunction with network pruning, we show that such a combination makes asubstantial improvement over each of the two techniques individually.

How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inference