3 papers
eess.AS2026
Test-Time Adaptation For Speech Enhancement Via Mask Polarization
Tobias Raichle, Erfan Amini, Bin Yang
Adapting speech enhancement (SE) models to unseen environments is crucial for practical deployments, yet test-time adaptation (TTA) for SE remains largely under-explored due to a l…
eess.AS2025
Test-Time Adaptation for Speech Enhancement via Domain Invariant Embedding Transformation
Tobias Raichle, Niels Edinger, Bin Yang
Deep learning-based speech enhancement models achieve remarkable performance when test distributions match training conditions, but often degrade when deployed in unpredictable rea…
cs.CV2024
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models
Mario Döbler, Robert A. Marsden, Tobias Raichle +1
In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models…