13 citations · 27 across the 5 of their papers we have counts for
6 papers · 1 filter
Stitched ViTs are Flexible Vision Backbones
Zizheng Pan, Jing Liu, Haoyu He +2
Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms o…
PTQD: Accurate Post-Training Quantization for Diffusion Models
Yefei He, Luping Liu, Jing Liu +3
Diffusion models have recently dominated image synthesis tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models les…
Scalable Vision Transformers with Hierarchical Pooling
Zizheng Pan, Bohan Zhuang, Jing Liu +2
The recently proposed Visual image Transformers (ViT) with pure attention have achieved promising performance on image recognition tasks, such as image classification. However, the…
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…
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…
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang +5
Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimi…