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
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation
Fanding Huang, Zihao Yao, Wenhui Zhou
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning
Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation
Mohammad Rostami, Dayuan Jian
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations
Serban Stan, Mohammad Rostami
Unsupervised Federated Domain Adaptation for Segmentation of MRI Images
Navapat Nananukul, Hamid Soltanian-zadeh, Mohammad Rostami
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen
MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept
Asbjørn Munk, Ao Ma, Mads Nielsen