  Compression of convolutional neural network models has recently been
dominated by pruning approaches. A class of previous works focuses solely on
pruning the unimportant filters to achieve network compression. Another
important direction is the design of sparsity-inducing constraints which has
also been explored in isolation. This paper presents a novel training scheme
based on composite constraints that prune redundant filters and minimize their
effect on overall network learning via sparsity promotion. Also, as opposed to
prior works that employ pseudo-norm-based sparsity-inducing constraints, we
propose a sparse scheme based on gradient counting in our framework. Our tests
on several pixel-wise segmentation benchmarks show that the number of neurons
and the memory footprint of networks in the test phase are significantly
reduced without affecting performance. MobileNetV3 and UNet, two well-known
architectures, are used to test the proposed scheme. Our network compression
method not only results in reduced parameters but also achieves improved
performance compared to MobileNetv3, which is an already optimized
architecture.

Computer Vision and Pattern Recognition