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
Privacy-Preserving Domain Adaptation of Semantic Parsers
Fatemehsadat Mireshghallah, Yu Su, Tatsunori Hashimoto, Jason Eisner, Richard Shin
Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models
Tommaso Mario Buonocore, Claudio Crema, Alberto Redolfi, Riccardo Bellazzi, Enea Parimbelli
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering
Dheeru Dua, Emma Strubell, Sameer Singh, Pat Verga
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision
Malo Alefsen de Boisredon d'Assier, Eugene Vorontsov, Samuel Kadoury
Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Junru Zhong, Yongcheng Yao, Donal G. Cahill, Fan Xiao, Siyue Li, Jack Lee, Kevin Ki-Wai Ho, Michael Tim-Yun Ong, James F. Griffith, Weitian Chen
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation
Wanqing Zhu, Jia-Li Yin, Bo-Hao Chen, Ximeng Liu
Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback
Ahmad Idrissi-Yaghir, Henning Schäfer, Nadja Bauer, Christoph M. Friedrich