no code implementations • 16 Apr 2024 • Hao Feng, Yuanzhe Jia, Ruijia Xu, Mukesh Prasad, Ali Anaissi, Ali Braytee
Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts.
1 code implementation • 8 Jun 2023 • Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.
1 code implementation • 21 Jan 2022 • Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, Tong Zhang
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
1 code implementation • ACL 2021 • Shizhe Diao, Ruijia Xu, Hongjin Su, Yilei Jiang, Yan Song, Tong Zhang
In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data.
Ranked #42 on
Time Series Forecasting
on ETTh1 (336) Multivariate
no code implementations • CVPR 2022 • Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang
In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.
1 code implementation • ECCV 2020 • Ganlong Zhao, Guanbin Li, Ruijia Xu, Liang Lin
Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance.
no code implementations • 18 Dec 2019 • Jihan Yang, Ruijia Xu, Ruiyu Li, Xiaojuan Qi, Xiaoyong Shen, Guanbin Li, Liang Lin
In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.
3 code implementations • ICCV 2019 • Ruijia Xu, Guanbin Li, Jihan Yang, Liang Lin
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Ranked #8 on
Domain Adaptation
on ImageCLEF-DA
1 code implementation • CVPR 2018 • Ruijia Xu, Ziliang Chen, WangMeng Zuo, Junjie Yan, Liang Lin
Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
no code implementations • ICCV 2017 • Zhouxia Wang, Tianshui Chen, Guanbin Li, Ruijia Xu, Liang Lin
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding.