Domain Adaptation
Domain adaptation addresses the challenge of applying machine learning models trained on one dataset (the source domain) to a different dataset with a different distribution (the target domain). Current research focuses on techniques like adversarial training, knowledge distillation, and optimal transport to bridge this domain gap, often employing transformer-based models, generative adversarial networks (GANs), and various meta-learning approaches. This field is crucial for improving the robustness and generalizability of machine learning models across diverse real-world applications, particularly in areas with limited labeled data such as medical imaging, natural language processing for low-resource languages, and personalized recommendation systems. The development of standardized evaluation frameworks is also a growing area of focus to ensure fair comparison and reproducibility of results.
Papers
Clustering-Based Validation Splits for Model Selection under Domain Shift
Andrea Napoli, Paul White
PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
Dingkang Yang, Jinjie Wei, Dongling Xiao, Shunli Wang, Tong Wu, Gang Li, Mingcheng Li, Shuaibing Wang, Jiawei Chen, Yue Jiang, Qingyao Xu, Ke Li, Peng Zhai, Lihua Zhang
Domain adaptation in small-scale and heterogeneous biological datasets
Seyedmehdi Orouji, Martin C. Liu, Tal Korem, Megan A. K. Peters
SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation
Yitao Zhao, Turgay Celik, Nanqing Liu, Feng Gao, Heng-Chao Li
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Shanshan Wang, Hao Zhou, Xun Yang, Zhenwei He, Mengzhu Wang, Xingyi Zhang, Meng Wang