This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version whose weights are constrained to be either powers of two or zero. Unlike existing methods which are struggled in noticeable accuracy loss, our INQ has the potential to resolve this issue, as benefiting from two innovations...
Authors
Yurong Chen
Senior Research Director & Principle Research Scientist, Cognitive Computing Lab, Intel Labs China
Aojun Zhou
Lin Xu
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