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Robust Multi-Source Batch Normalisation for Test-Time Batch Adaptation
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DOI：10.1049/cit2.70159.png)
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摘要
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Test-Time Batch Adaptation (TTBA) aims to adapt a pre-trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre-trained model or the target data in a one-to-one alignment paradigm. However, these one-to-one alignment paradigms assume that the source domain may share the same knowledge with the target domain. It becomes ineffective when the target domain is different from the source domain. In this paper, a multi-source batch normalisation method is introduced. Specifically, spectral similarity clustering selects a small set of representative source domains, spectral entropy-guided weighting combines source statistics based on their relevance to the target domain, and batch-wise re-initialisation with unsupervised refinement stabilises updates. Extensive experiments on multiple benchmark datasets verify the effectiveness of the proposed method, achieving superior performance over state-of-the-art TTBA methods, especially in scenarios with small target batches.
Keyword:
multi-source batch normalisation
source domain selection
spectral entropy weighting
test-time batch adaptation
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Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training重新审视真实测试时间训练：基于锚定聚类正则化自训练的顺序推理与适应
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