167 citations · 914 across the 103 of their papers we have counts for
5 papers · 1 filter
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Chunlei Liu, Wenrui Ding, Xin Xia +5
Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips b…
Structured Binary Neural Networks for Image Recognition
Bohan Zhuang, Chunhua Shen, Mingkui Tan +3
We propose methods to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models that are specifically friendly to mobile d…
Auxiliary Learning for Deep Multi-task Learning
Yifan Liu, Bohan Zhuang, Chunhua Shen +2
Multi-task learning (MTL) is an efficient solution to solve multiple tasks simultaneously in order to get better speed and performance than handling each single-task in turn. The m…
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
Bohan Zhuang, Jing Liu, Mingkui Tan +3
This paper tackles the problem of training a deep convolutional neural network of both low-bitwidth weights and activations. Optimizing a low-precision network is very challenging…
Training Quantized Neural Networks with a Full-precision Auxiliary Module
Bohan Zhuang, Lingqiao Liu, Mingkui Tan +2
In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-dif…