5 papers
Spatial Re-parameterization for N:M Sparsity
Yuxin Zhang, Mingbao Lin, Mingliang Xu +2
This paper presents a Spatial Re-parameterization (SpRe) method for the N:M sparsity. SpRe stems from an observation regarding the restricted variety in spatial sparsity of convolu…
Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study
Lirui Zhao, Yuxin Zhang, Fei Chao +1
The poor cross-architecture generalization of dataset distillation greatly weakens its practical significance. This paper attempts to mitigate this issue through an empirical study…
MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization
Yunshan Zhong, Yuyao Zhou, Fei Chao +1
Arbitrary bit-width network quantization has received significant attention due to its high adaptability to various bit-width requirements during runtime. However, in this paper, w…
UniPTS: A Unified Framework for Proficient Post-Training Sparsity
Jingjing Xie, Yuxin Zhang, Mingbao Lin +3
Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant perfo…
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
Yuxin Zhang, Lirui Zhao, Mingbao Lin +6
The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towar…