  In recent years, most of the deep learning solutions are targeted to be
deployed in mobile devices. This makes the need for development of lightweight
models all the more imminent. Another solution is to optimize and prune regular
deep learning models. In this paper, we tackle the problem of CNN model pruning
with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters.
We propose two novel algorithms to rank and prune redundant filters which
contribute similar activation maps to the output. One of the key features of
our method is that there is no need of finetuning after training the model.
Both the training and pruning process is completed simultaneously. We benchmark
our method on two of the most popular CNN models - ResNet and VGG and record
their performance on the CIFAR-10 dataset.

Machine Learning;Computer Vision and Pattern Recognition