arXiv Machine Learning By Ashish Anand Shukla, Rini Smita Thakur, Aryan Das, Vinod K. Kurmi

Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift

Read the original on arXiv Machine Learning →

arXiv:2608. 15037v1 Announce Type: cross Abstract: Audio-Text Foundation Models (ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-based Test-Time Adaptation (TTA), which reinforces noise rather than signal, or on prompt tuning that requires privileged noise annotations unavailable at inference.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.