Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks

作者: | Kohei Hayashi | Taiki Yamaguchi | Yohei Sugawara | Shin-ichi Maeda |

摘要:Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in practice, and no extensive comparisons have been made between available methods. Previous studies have not determined how many decompositions are available, nor which of them is optimal. In this study, we first characterize a decomposition class specific to CNNs by adopting a flexible graphical notation. The class includes such well-known CNN modules as depthwise separable convolution layers and bottleneck layers, but also previously unknown modules with nonlinear activations. We also experimentally compare the tradeoff between prediction accuracy and time/space complexity for modules found by enumerating all possible decompositions, or by using a neural architecture search. We find some nonlinear decompositions outperform existing ones.

论文地址

https://arxiv.org/abs/1908.04471v2

下载地址

https://arxiv.org/pdf/1908.04471v2.pdf

全部源码

https://github.com/pfnet-research/einconv 类型: pytorch

model compression neural architecture search

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Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks