Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

CVPR 2019 Bohan ZhuangChunhua ShenMingkui TanLingqiao LiuIan Reid

In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices with limited power capacity and computation resources. Previous works on quantizing CNNs seek to approximate the floating-point information using a set of discrete values, which we call value approximation, but typically assume the same architecture as the full-precision networks... (read more)

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