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

BRIEF: Backward Reduction of CNNs with Information Flow Analysis