67 citations · 67 across the 5 of their papers we have counts for
4 papers · 1 filter
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
Xuewen Liu, Zhikai Li, Minhao Jiang +3
Model quantization is a promising method for accelerating and compressing diffusion models. Nevertheless, since post-training quantization (PTQ) fails catastrophically at low-bit c…
Cluster Regularized Quantization for Deep Networks Compression
Yiming Hu, Jianquan Li, Xianlei Long +4
Deep neural networks (DNNs) have achieved great success in a wide range of computer vision areas, but the applications to mobile devices is limited due to their high storage and co…
Multi-loss-aware Channel Pruning of Deep Networks
Yiming Hu, Siyang Sun, Jianquan Li +3
Channel pruning, which seeks to reduce the model size by removing redundant channels, is a popular solution for deep networks compression. Existing channel pruning methods usually…
A novel channel pruning method for deep neural network compression
Yiming Hu, Siyang Sun, Jianquan Li +2
In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-cons…