no code implementations • 9 Dec 2023 • Yuewei Yang, Xiaoliang Dai, Jialiang Wang, Peizhao Zhang, Hongbo Zhang
By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies.
no code implementations • ICCV 2023 • Yuewei Yang, Hai Li, Yiran Chen
In recent years, discriminative self-supervised methods have made significant strides in advancing various visual tasks.
no code implementations • 11 Nov 2022 • Yuewei Yang, Jingwei Sun, Ang Li, Hai Li, Yiran Chen
In this work, we propose a novel method, FedStyle, to learn a more generalized global model by infusing local style information with local content information for contrastive learning, and to learn more personalized local models by inducing local style information for downstream tasks.
1 code implementation • 2 Jul 2021 • Qing Guo, Junya Chen, Dong Wang, Yuewei Yang, Xinwei Deng, Lawrence Carin, Fan Li, Jing Huang, Chenyang Tao
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
no code implementations • 6 Dec 2020 • Dong Wang, Yuewei Yang, Chenyang Tao, Zhe Gan, Liqun Chen, Fanjie Kong, Ricardo Henao, Lawrence Carin
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts.
no code implementations • 11 Feb 2020 • Yuewei Yang, Kevin J Liang, Lawrence Carin
These missing annotations can be problematic, as the standard cross-entropy loss employed to train object detection models treats classification as a positive-negative (PN) problem: unlabeled regions are implicitly assumed to be background.