1 citations · 2 across the 5 of their papers we have counts for
6 papers
High-Fidelity Pruning for Large Language Models
Yijun Zhu, Jianxin Wang, Chengchao Shen
Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, yet their significant computational and memory requirements present major chall…
Multiple Object Stitching for Unsupervised Representation Learning
Chengchao Shen, Dawei Liu, Jianxin Wang
Contrastive learning for single object centric images has achieved remarkable progress on unsupervised representation, but suffering inferior performance on the widespread images w…
Diversity-Guided MLP Reduction for Efficient Large Vision Transformers
Chengchao Shen, Hourun Zhu, Gongfan Fang +2
Transformer models achieve excellent scaling property, where the performance is improved with the increment of model capacity. However, large-scale model parameters lead to an unaf…
Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning
Chengchao Shen, Jianzhong Chen, Jianxin Wang
The existing contrastive learning methods mainly focus on single-grained representation learning, e.g., part-level, object-level or scene-level ones, thus inevitably neglecting the…
Inter-Instance Similarity Modeling for Contrastive Learning
Chengchao Shen, Dawei Liu, Hao Tang +2
The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich inter-instance s…
Modeling Global Distribution for Federated Learning with Label Distribution Skew
Tao Sheng, Chengchao Shen, Yuan Liu +3
Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more…