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cs.LG2026
GMM-COMET: Continual Source-Free Universal Domain Adaptation via a Mean Teacher and Gaussian Mixture Model-Based Pseudo-Labeling
Pascal Schlachter, Bin Yang
Unsupervised domain adaptation tackles the problem that domain shifts between training and test data impair the performance of neural networks in many real-world applications. Ther…
cs.LG2025
Analysis of Pseudo-Labeling for Online Source-Free Universal Domain Adaptation
Pascal Schlachter, Jonathan Fuss, Bin Yang
A domain (distribution) shift between training and test data often hinders the real-world performance of deep neural networks, necessitating unsupervised domain adaptation (UDA) to…