Source Free

Source-free domain adaptation (SFDA) focuses on adapting machine learning models, particularly for image segmentation tasks, to new, unseen data domains without access to the original training data. Current research emphasizes techniques like self-training with pseudo-labels, often incorporating mechanisms to reduce noise and bias in these labels through methods such as neighbor denoising and entropy minimization. This area is crucial for addressing privacy concerns and data limitations in various applications, including medical imaging and autonomous driving, where access to original training data may be restricted or impractical. The development of robust and efficient SFDA methods is driving advancements in model adaptation and improving the generalizability of machine learning models across diverse data distributions.