167 citations · 914 across the 103 of their papers we have counts for
8 papers · 1 filter
Fully Quantized Image Super-Resolution Networks
Hu Wang, Peng Chen, Bohan Zhuang +1
With the rising popularity of intelligent mobile devices, it is of great practical significance to develop accurate, realtime and energy-efficient image Super-Resolution (SR) infer…
FATNN: Fast and Accurate Ternary Neural Networks
Peng Chen, Bohan Zhuang, Chunhua Shen
Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision c…
AQD: Towards Accurate Fully-Quantized Object Detection
Peng Chen, Jing Liu, Bohan Zhuang +2
Network quantization allows inference to be conducted using low-precision arithmetic for improved inference efficiency of deep neural networks on edge devices. However, designing a…
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu, Xue Geng, Zhijian Duan +6
Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the \textit{teacher}) into another model (the \textit{stude…
Generative Low-bitwidth Data Free Quantization
Shoukai Xu, Haokun Li, Bohan Zhuang +4
Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedde…
Switchable Precision Neural Networks
Luis Guerra, Bohan Zhuang, Ian Reid +1
Instantaneous and on demand accuracy-efficiency trade-off has been recently explored in the context of neural networks slimming. In this paper, we propose a flexible quantization s…
Automatic Pruning for Quantized Neural Networks
Luis Guerra, Bohan Zhuang, Ian Reid +1
Neural network quantization and pruning are two techniques commonly used to reduce the computational complexity and memory footprint of these models for deployment. However, most e…
Discrimination-aware Network Pruning for Deep Model Compression
Jing Liu, Bohan Zhuang, Zhuangwei Zhuang +4
We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with…