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- W2894788122 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." @default.
- W2894788122 created "2018-10-12" @default.
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- W2894788122 date "2018-12-01" @default.
- W2894788122 modified "2023-10-16" @default.
- W2894788122 title "BRIEF: Backward Reduction of CNNs with Information Flow Analysis" @default.
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- W2894788122 doi "https://doi.org/10.1109/aivr.2018.00014" @default.
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