Source Free Domain Adaptation

Source-free domain adaptation (SFDA) tackles the challenge of adapting a pre-trained model to a new, unlabeled target domain without access to the original source data, addressing privacy and data scarcity issues. Current research focuses on improving the robustness and accuracy of SFDA methods across various tasks, including image classification, object detection, and time-series analysis, employing techniques like self-training with pseudo-labels, adversarial training, and active learning, often within teacher-student or contrastive learning frameworks. These advancements are significant for applications where source data is unavailable or ethically restricted, enabling model deployment in diverse and sensitive contexts while minimizing the need for extensive new data annotation. The development of more efficient and reliable SFDA techniques is crucial for advancing various fields, including medical imaging, remote sensing, and autonomous driving.