June 11, 2024, 4:46 a.m. | Qinting Jiang, Chuyang Ye, Dongyan Wei, Yuan Xue, Jingyan Jiang, Zhi Wang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05413v1 Announce Type: new
Abstract: Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality of Experience (QoE) for multimedia applications. Existing test-time adaptation (TTA) methods are challenged by dynamic, multiple test distributions within batches. This work provides a new perspective on analyzing batch normalization techniques through class-related and class-irrelevant features, our observations reveal combining source and test batch normalization statistics robustly characterizes target distributions. However, …

abstract advanced applications arxiv cs.ai cs.cv cs.lg cs.mm distribution domains dynamic experience multimedia multiple neighbors networks neural networks performance progress quality test training type world

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