Progressive Learning of Low-Precision Networks

Zhengguang Zhou; Wengang Zhou; Xutao Lv; Xuan Huang; Xiaoyu Wang; Houqiang Li

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arXiv · 1905.11781

Progressive Learning of Low-Precision Networks

Zhengguang Zhou·Wengang Zhou·Xutao Lv·Xuan Huang·Xiaoyu Wang·Houqiang Li

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Abstract

Recent years have witnessed the great advance of deep learning in a variety of vision tasks. Many state-of-the-art deep neural networks suffer from large size and high complexity, which makes it difficult to deploy in resource-limited platforms such as mobile devices. To this end, low-precision neural networks are widely studied which quantize weights or activations into the low-bit format. Though being efficient, low-precision networks are usually hard to train and encounter severe accuracy degradation. In this paper, we propose a new training strategy through expanding low-precision networks during training and removing the expanded parts for network inference. First, we equip each low-precision convolutional layer with an ancillary full-precision convolutional layer based on a low-precision network structure, which could guide the network to good local minima. Second, a decay method is introduced to reduce the output of the added full-precision convolution gradually, which keeps the resulted topology structure the same to the original low-precision one. Experiments on SVHN, CIFAR and ILSVRC-2012 datasets prove that the proposed method can bring faster convergence and higher accuracy for low-precision neural networks.

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BibTeX RIS

Zhengguang Zhou, Wengang Zhou, Xutao Lv, Xuan Huang, Xiaoyu Wang, Houqiang Li. 2019-05-28. Progressive Learning of Low-Precision Networks. https://arxiv.org/abs/1905.11781

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