Abstract
Convolutional Neural Networks (CNN) have achieved excellent performance in the processing of high-resolution images. Most of these networks contain many deep layers in quest of greater segmentation performance. However, over-sized CNN models result in overwhelming memory usage and large inference costs. Earlier studies have revealed that over-sized deep neural models tend to deal with abundant ...
Abstract
Convolutional Neural Networks (CNN) have achieved excellent performance in the processing of high-resolution images. Most of these networks contain many deep layers in quest of greater segmentation performance. However, over-sized CNN models result in overwhelming memory usage and large inference costs. Earlier studies have revealed that over-sized deep neural models tend to deal with abundant redundant filters that are very similar and provide tiny or no contribution in accelerating the inference of the model. Therefore, we have proposed a novel optimal-score-based filter pruning (OSFP) approach to prune redundant filters according to their relative similarity in feature space. OSFP not only speeds up learning in the network but also eradicates redundant filters leading to improvement in the segmentation performance. We empirically demonstrate on widely used segmentation network models (TernausNet, classical U-Net and VGG16 U-Net) and benchmark datasets (Inria Aerial Image Labeling Dataset and Aerial Imagery for Roof Segmentation (AIRS)) that computation costs (in terms of Float Point Operations (FLOPs) and parameters) are reduced significantly, while maintaining or even improving accuracy.
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