Domain Generalization
Domain generalization (DG) aims to train machine learning models that perform well on unseen data, overcoming the limitations of models trained and tested on similar data distributions. Current research focuses on improving model robustness through techniques like self-supervised learning, data augmentation (including novel methods like style prompting and spectrum synthesis), and the use of foundation models and parameter-efficient fine-tuning. These advancements are crucial for deploying reliable AI systems in real-world applications where data variability is inevitable, particularly in fields like medical imaging, autonomous systems, and natural language processing.
386papers
Papers
April 3, 2025
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation
Alexandre Misrahi, Nadezhda Chirkova, Maxime Louis, Vassilina NikoulinaEPFL●NAVER LABS EuropeGenerative Classifier for Domain Generalization
Shaocong Long, Qianyu Zhou, Xiangtai Li, Chenhao Ying, Yunhai Tong, Lizhuang Ma, Yuan Luo, Dacheng TaoShanghai Jiao Tong University●Jilin University●Nanyang Technological University●Peking University
March 30, 2025
March 21, 2025
A Language Anchor-Guided Method for Robust Noisy Domain Generalization
Zilin Dai, Lehong Wang, Fangzhou Lin, Yidong Wang, Zhigang Li, Kazunori D Yamada, Ziming Zhang, Wang LuWorcester Polytechnic Institute●Carnegie Mellon University●Tohoku University●Tsinghua University●Peking UniversityCasual Inference via Style Bias Deconfounding for Domain Generalization
Jiaxi Li, Di Lin, Hao Chen, Hongying Liu, Liang Wan, Wei FengTianjin University●The Hong Kong University of Science and Technology
March 19, 2025
V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception
Baolu Li, Zongzhe Xu, Jinlong Li, Xinyu Liu, Jianwu Fang, Xiaopeng Li, Hongkai YuCleveland State University●Carnegie Mellon University●Texas A&M University●Xi’an Jiaotong University●University of Wisconsin-MadisonWhen Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach
Vaibhav Rathore, Shubhranil B, Saikat Dutta, Sarthak Mehrotra, Zsolt Kira, Biplab BanerjeeIIT Bombay●IITB-Monash Research Academy●Georgia Institute of Technology
March 17, 2025
Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization
Hao Li, Yubin Xiao, Ke Liang, Mengzhu Wang, Long Lan, Kenli Li, Xinwang LiuNational University of Defense Technology●Jilin University●Hunan UniversityTest-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation
Xingguo Lv, Xingbo Dong, Liwen Wang, Jiewen Yang, Lei Zhao, Bin Pu, Zhe Jin, Xuejun Li
March 9, 2025
Revisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets
Hung Q. Vo, Samira Zare, Son T. Ly, Lin Wang, Chika F. Ezeana, Xiaohui Yu, Kelvin K. Wong, Stephen T.C. Wong, Hien V. NguyenUniversity of Houston●Houston Methodist Cancer CenterWhat's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization
Xavier Thomas, Deepti GhadiyaramBoston University●Runway