7 citations · 7 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Memory-Efficient Pseudo-Labeling for Online Source-Free Universal Domain Adaptation using a Gaussian Mixture Model
Pascal Schlachter, Simon Wagner, Bin Yang
In practice, domain shifts are likely to occur between training and test data, necessitating domain adaptation (DA) to adjust the pre-trained source model to the target domain. Rec…
COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation
Pascal Schlachter, Bin Yang
In real-world applications, there is often a domain shift from training to test data. This observation resulted in the development of test-time adaptation (TTA). It aims to adapt a…
Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results
Pascal Schlachter, Zhibin Yu, Naveed Iqbal +3
To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy,…