Backward Reduction of CNN Models with Information Flow Analysis
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This paper proposes backward reduction, an algorithm that explores the compact CNN design from the information flow perspective. This algorithm can remove substantial non-zero weighting parameters (redundant neural channels) by considering the network dynamic behavior, which the traditional model compaction techniques cannot achieve, to reduce the size of a model. With the aid of our proposed algorithm, we achieve significant model reduction results of ResNet-34 in ImageNet scale (32.3 state-of-the-art result (10.8 SqueezeNet and MobileNet, we still achieve additional 10.81 reduction, respectively, with negligible performance degradation.
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