Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression

2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)(2021)

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Abstract
Low-rank tensor decomposition is a widely-used strategy to compress convolutional neural networks (CNNs). Existing learning-based decomposition methods encourage low-rank filter weights via regularizer of filters’ pair-wise force or nuclear norm during training. However, these methods can not obtain the satisfactory low-rank structure. We propose a new method with an adaptive rank penalty to learn...
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Key words
low-rank decomposition,network compression,learning-based decomposition,adaptive rank penalty
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