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
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
Yao Ma, Samuel Louvan, Zhunxuan Wang
Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Air Quality Sensor Fusion
Keivan Faghih Niresi, Ismail Nejjar, Olga Fink
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Xinyang Huang, Chuang Zhu, Bowen Zhang, Shanghang Zhang
Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway Tree Segmentation
Maxime Jacovella, Ali Keshavarzi, Elsa Angelini
Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
Shuchang Lyu, Qi Zhaoa, Guangliang Cheng, Yiwei He, Zheng Zhou, Guangbiao Wang, Zhenwei Shi
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination
Salman Rakin, Md. A.R. Shibly, Zahin M. Hossain, Zeeshan Khan, Md. Mostofa Akbar
Time and Frequency Synergy for Source-Free Time-Series Domain Adaptations
Muhammad Tanzil Furqon, Mahardhika Pratama, Ary Mazharuddin Shiddiqi, Lin Liu, Habibullah Habibullah, Kutluyil Dogancay
Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment
Indrajeet Ghosh, Garvit Chugh, Abu Zaher Md Faridee, Nirmalya Roy