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 - Page 13
EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation
Wanqi Yang, Haoran Wang, Lei Wang, Ge Song, Yang GaoGeodesic Optimization for Predictive Shift Adaptation on EEG data
Apolline Mellot, Antoine Collas, Sylvain Chevallier, Alexandre Gramfort, Denis A. EngemannPOSTURE: Pose Guided Unsupervised Domain Adaptation for Human Body Part Segmentation
Arindam Dutta, Rohit Lal, Yash Garg, Calvin-Khang Ta, Dripta S. Raychaudhuri, Hannah Dela Cruz, Amit K. Roy-Chowdhury
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
Zihao Luo, Xiangde Luo, Zijun Gao, Guotai WangMulti-Task Domain Adaptation for Language Grounding with 3D Objects
Penglei Sun, Yaoxian Song, Xinglin Pan, Peijie Dong, Xiaofei Yang, Qiang Wang, Zhixu Li, Tiefeng Li, Xiaowen Chu
STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning
Yanan Zhang, Chao Zhou, Di HuangProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
Nazanin Moradinasab, Laura S. Shankman, Rebecca A. Deaton, Gary K. Owens, Donald E. BrownZero-shot domain adaptation based on dual-level mix and contrast
Yu Zhe, Jun SakumaApplying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over
Atsunori Ogawa, Naoyuki Kamo, Kohei Matsuura, Takanori Ashihara, Takafumi Moriya, Takatomo Kano, Naohiro Tawara, Marc DelcroixDivide, Ensemble and Conquer: The Last Mile on Unsupervised Domain Adaptation for Semantic Segmentation
Tao Lian, Jose L. Gómez, Antonio M. López
Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation
Shamane Siriwardhana, Mark McQuade, Thomas Gauthier, Lucas Atkins, Fernando Fernandes Neto, Luke Meyers, Anneketh Vij, Tyler Odenthal+370B-parameter large language models in Japanese medical question-answering
Issey Sukeda, Risa Kishikawa, Satoshi KoderaWord Matters: What Influences Domain Adaptation in Summarization?
Yinghao Li, Siyu Miao, Heyan Huang, Yang Gao