7 citations · 15 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…