Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation

• URL: http://arxiv.org/abs/2508.09223v1
• Date: Mon, 11 Aug 2025 21:55:53 GMT
• Title: Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation
• Authors: Sameer Ambekar, Daniel M. Lang, Julia A. Schnabel,
• Abstract summary: We propose Hierarchical Adaptive Networks with Task Vectors (Hi-Vec)<n>Hi-Vec allows existing methods to adapt to shifts of varying complexity.<n>We rigorously evaluate the performance of Hi-Vec in challenging scenarios and on multiple target datasets.
• Score: 3.3834108313265916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time adaptation allows pretrained models to adjust to incoming data streams, addressing distribution shifts between source and target domains. However, standard methods rely on single-dimensional linear classification layers, which often fail to handle diverse and complex shifts. We propose Hierarchical Adaptive Networks with Task Vectors (Hi-Vec), which leverages multiple layers of increasing size for dynamic test-time adaptation. By decomposing the encoder's representation space into such hierarchically organized layers, Hi-Vec, in a plug-and-play manner, allows existing methods to adapt to shifts of varying complexity. Our contributions are threefold: First, we propose dynamic layer selection for automatic identification of the optimal layer for adaptation to each test batch. Second, we propose a mechanism that merges weights from the dynamic layer to other layers, ensuring all layers receive target information. Third, we propose linear layer agreement that acts as a gating function, preventing erroneous fine-tuning by adaptation on noisy batches. We rigorously evaluate the performance of Hi-Vec in challenging scenarios and on multiple target datasets, proving its strong capability to advance state-of-the-art methods. Our results show that Hi-Vec improves robustness, addresses uncertainty, and handles limited batch sizes and increased outlier rates.

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