67 citations · 67 across the 5 of their papers we have counts for
5 papers
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…
K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences
Zhikai Li, Xuewen Liu, Dongrong Joe Fu +4
The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank mo…
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…