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arXiv · 1807.05726

BRIEF: Backward Reduction of CNNs with Information Flow Analysis

Yu-Hsun Lin·Chun-Nan Chou·Edward Y. Chang
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Abstract

This paper proposes BRIEF, a backward reduction algorithm that explores compact CNN-model designs from the information flow perspective. This algorithm can remove substantial non-zero weighting parameters (redundant neural channels) of a network by considering its dynamic behavior, which traditional model-compaction techniques cannot achieve. With the aid of our proposed algorithm, we achieve significant model reduction on ResNet-34 in the ImageNet scale (32.3% reduction), which is 3X better than the previous result (10.8%). Even for highly optimized models such as SqueezeNet and MobileNet, we can achieve additional 10.81% and 37.56% reduction, respectively, with negligible performance degradation.

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Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang. 2018-07-16. BRIEF: Backward Reduction of CNNs with Information Flow Analysis. https://arxiv.org/abs/1807.05726

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