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Publication . Conference object . 2020

Fractional Step Discriminant Pruning: A Filter Pruning Framework for Deep Convolutional Neural Networks

Nikolaos Gkalelis; Vasileios Mezaris;
Open Access
Published: 06 Jul 2020
Publisher: IEEE
Abstract

In this paper, a novel pruning framework is introduced to compress noisy or less discriminant filters in small fractional steps, in deep convolutional networks. The proposed framework utilizes a class-separability criterion that can exploit effectively the labeling information in annotated training sets. Additionally, an asymptotic schedule for the pruning rate and scaling factor is adopted so that the selected filters’ weights collapse gradually to zero, providing improved robustness. Experimental results on the CIFAR-10, Google speech commands (GSC) and ImageNet32 (a downsampled version of ILSVRC-2012) show the efficacy of the proposed approach.

Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Pattern recognition Schedule Convolutional neural network Robustness (computer science) Discriminant Artificial intelligence business.industry business

Subjects

Deep convolutional neural networks, asymptotic filter pruning, class-separability criteria

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