Transform Quantization for CNN Compression

2 Sep 2020 Sean I. Young Wang Zhe David Taubman Bernd Girod

In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization. Previous CNN quantization techniques tend to ignore the joint statistics of weights and activations, producing sub-optimal CNN performance at a given quantization bit-rate, or consider their joint statistics during training only and do not facilitate efficient compression of already trained CNN models... (read more)

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