  The challenge of speeding up deep learning models during the deployment phase
has been a large, expensive bottleneck in the modern tech industry. In this
paper, we examine the use of both regularization and pruning for reduced
computational complexity and more efficient inference in Deep Neural Networks
(DNNs). In particular, we apply mixup and cutout regularizations and soft
filter pruning to the ResNet architecture, focusing on minimizing
floating-point operations (FLOPs). Furthermore, by using regularization in
conjunction with network pruning, we show that such a combination makes a
substantial improvement over each of the two techniques individually.
