Random Bias Initialization Improves Quantized Training

Xinlin Li; Vahid Partovi Nia

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arXiv · 1909.13446

Random Bias Initialization Improves Quantized Training

Xinlin Li·Vahid Partovi Nia

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Abstract

Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful full-precision training and binary training. We bring some insights about why this accuracy drop exists and call for a better understanding of binary network geometry. We start with analyzing full-precision neural networks with ReLU activation and compare it with its binarized version. This comparison suggests to initialize networks with random bias, a counter-intuitive remedy.

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Xinlin Li, Vahid Partovi Nia. 2019-09-30. Random Bias Initialization Improves Quantized Training. https://arxiv.org/abs/1909.13446

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