5 papers
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation
Shuai Wang, Daoan Zhang, Zhe Tang +2
Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks. Current post-training methods usually rely on human-annot…
Mapping Text to Multiplex Graph: Prompt Compression as Lévy Walk-Guided Graph Pruning
Yaxin Gao, Yao Lu, Jinhong Deng +7
Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple lo…
EMP: Enhance Memory in Data Pruning
Jinying Xiao, Ping Li, Jie Nie +6
Recently, large language and vision models have shown strong performance, but due to high pre-training and fine-tuning costs, research has shifted towards faster training via datas…
LNPT: Label-free Network Pruning and Training
Jinying Xiao, Ping Li, Zhe Tang +1
Pruning before training enables the deployment of neural networks on smart devices. By retaining weights conducive to generalization, pruned networks can be accommodated on resourc…
SEVEN: Pruning Transformer Model by Reserving Sentinels
Jinying Xiao, Ping Li, Jie Nie +1
Large-scale Transformer models (TM) have demonstrated outstanding performance across various tasks. However, their considerable parameter size restricts their applicability, partic…