7 citations · 15 across the 6 of their papers we have counts for
6 papers
Mesa: A Memory-saving Training Framework for Transformers
Zizheng Pan, Peng Chen, Haoyu He +3
There has been an explosion of interest in designing high-performance Transformers. While Transformers have delivered significant performance improvements, training such networks i…
Single-path Bit Sharing for Automatic Loss-aware Model Compression
Jing Liu, Bohan Zhuang, Peng Chen +3
Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then c…
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…
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…