6 citations · 7 across the 6 of their papers we have counts for
6 papers
Temporal Feature Matters: A Framework for Diffusion Model Quantization
Yushi Huang, Ruihao Gong, Xianglong Liu +4
The Diffusion models, widely used for image generation, face significant challenges related to their broad applicability due to prolonged inference times and high memory demands. E…
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Yan Wang, Yuhang Li, Ruihao Gong +8
Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different sy…
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Xiuying Wei, Yunchen Zhang, Yuhang Li +4
Post-training quantization~(PTQ) of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outl…
Real World Robustness from Systematic Noise
Yan Wang, Yuhang Li, Ruihao Gong
Systematic error, which is not determined by chance, often refers to the inaccuracy (involving either the observation or measurement process) inherent to a system. In this paper, w…
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
Yuhang Li, Feng Zhu, Ruihao Gong +5
User data confidentiality protection is becoming a rising challenge in the present deep learning research. Without access to data, conventional data-driven model compression faces…
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Mingzhu Shen, Feng Liang, Ruihao Gong +6
Quantization Neural Networks (QNN) have attracted a lot of attention due to their high efficiency. To enhance the quantization accuracy, prior works mainly focus on designing advan…