Deep neural network compression through interpretability-based filter pruning.

• Filters are visualized by the activation maximization to explain functions of filters. • DNNs are compressed based on the visualization results. • The redundant filters are measured based on the color and texture similarities. • The repetitive and invalid filters can be pruned by optimization. This paper proposes a method to compress deep neural networks (DNNs) based on interpretability. For a trained DNN model, the activation maximization technique is first used to visualize every filter of the DNN model. Then, a single-layer filter pruning approach is introduced from what is learned by visualization. The entire DNN model is compressed layer by layer by using the single-layer filter pruning method in which the compression of the current layer is based on the compression of the preceding layers. Importantly, in addition to effective compression, the proposed method renders a better interpretation of the deep learning process. With a 60 % compression rate of the VGG-16, our method achieves 0.8429 Top-1 accuracy under CIFAR-10, with a slight accuracy drop of only 0.0322, and the storage space of the model can be compressed to 9.42 Mb. For a modern DNN model such as ResNet50, our visualization-based filter pruning method is significantly better than other pruning strategies in different convolutional layers under different compression rates and the larger ImageNet dataset. After pruning, the computation cost and storage requirement of the DNN can be significantly reduced, which means that complex DNN models can be easily implemented in small mobile devices, thus enabling the efficient use of DNNs in the Internet of Things technologies. [ABSTRACT FROM AUTHOR]