no code implementations • 11 Jan 2024 • Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka
Based on those powerful LLMs, the model fine-tuned with domain-specific datasets posseses more specialized knowledge and thus is more practical like medical LLMs.
no code implementations • 2 Mar 2023 • Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, Weiran Huang
Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability.
no code implementations • ICCV 2023 • Manyi Zhang, Xuyang Zhao, Jun Yao, Chun Yuan, Weiran Huang
In this paper, to handle the problem and address the limitations of prior works, we propose a representation calibration method RCAL.
no code implementations • 19 Sep 2022 • Huiyuan Wang, Xuyang Zhao, Wei Lin
In this work, we consider parameter estimation in federated learning with data distribution and communication heterogeneity, as well as limited computational capacity of local devices.
1 code implementation • 1 Nov 2021 • Weiran Huang, Mingyang Yi, Xuyang Zhao, Zihao Jiang
It reveals that the generalization ability of contrastive self-supervised learning is related to three key factors: alignment of positive samples, divergence of class centers, and concentration of augmented data.
1 code implementation • 30 Oct 2017 • Xingwei Cao, Xuyang Zhao, Qibin Zhao
Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models.