Domain Shift
Domain shift, the discrepancy between training and deployment data distributions, significantly degrades machine learning model performance. Current research focuses on developing robust algorithms and model architectures, such as U-Nets, Swin Transformers, and diffusion models, to mitigate this issue through techniques like distribution alignment, adversarial training, and knowledge distillation. These efforts are crucial for improving the reliability and generalizability of machine learning models across diverse real-world applications, particularly in medical imaging, autonomous driving, and natural language processing, where data heterogeneity is common. The ultimate goal is to create models that generalize effectively to unseen data, reducing the need for extensive retraining and improving the practical impact of AI systems.
Papers
FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
Yu Tian, Congcong Wen, Min Shi, Muhammad Muneeb Afzal, Hao Huang, Muhammad Osama Khan, Yan Luo, Yi Fang, Mengyu Wang
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant Factors
Jingyi Yang, Zitong Yu, Xiuming Ni, Jia He, Hui Li
Test-time adaptation for geospatial point cloud semantic segmentation with distinct domain shifts
Puzuo Wang, Wei Yao, Jie Shao, Zhiyi He
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
Yuxiang Yang, Lu Wen, Xinyi Zeng, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Yan Wang