4 papers
Distilling Image Prototypes for Guided Test-Time Adaptation
Liwen Wang, Xingbo Dong, Iman Yi Liao +4
Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from noisy pseudo-labels and catastro…
AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratch
Weichuang Shao, Iman Yi Liao, Tomas Henrique Bode Maul +1
Recent foundational models, SSAST, EAT, HuBERT, Qwen-Audio, and Audio Flamingo, achieve top-tier results across standard audio benchmarks but are limited by fixed input rates and d…
DHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation
Weichuang Shao, Iman Yi Liao, Tomas Henrique Bode Maul +1
Existing Test-time Adaptation (TTA) studies rely heavily on static and homogeneous corruption protocols, such as ImageNet-C and CIFAR-10-C/100-C, leading to inconsistent evaluation…
An Investigation of Test-time Adaptation for Audio Classification under Background Noise
Weichuang Shao, Iman Yi Liao, Tomas Henrique Bode Maul +1
Domain shift is a prominent problem in Deep Learning, causing a model pre-trained on a source dataset to suffer significant performance degradation on test datasets. This research…