3 papers
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.RO2025
Indoor Positioning Based on Active Radar Sensing and Passive Reflectors: Reflector Placement Optimization
Sven Hinderer, Pascal Schlachter, Zhibin Yu +2
We extend our work on a novel indoor positioning system (IPS) for autonomous mobile robots (AMRs) based on radar sensing of local, passive radar reflectors. Through the combination…
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…