FRET: Feature Redundancy Elimination for Test Time Adaptation

• URL: http://arxiv.org/abs/2505.10641v1
• Date: Thu, 15 May 2025 18:22:56 GMT
• Title: FRET: Feature Redundancy Elimination for Test Time Adaptation
• Authors: Linjing You, Jiabao Lu, Xiayuan Huang, Xiangli Nie,
• Abstract summary: Test-Time Adaptation (TTA) aims to enhance the generalization of deep learning models when faced with test data that exhibits distribution shifts from the training data.<n>In practice, we observe that feature redundancy in embeddings tends to increase as domain shifts intensify in TTA.<n>We introduce Feature Redundancy Elimination for Test-time Adaptation (FRET), a novel perspective for TTA.
• Score: 4.793572485305334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-Time Adaptation (TTA) aims to enhance the generalization of deep learning models when faced with test data that exhibits distribution shifts from the training data. In this context, only a pre-trained model and unlabeled test data are available, making it particularly relevant for privacy-sensitive applications. In practice, we observe that feature redundancy in embeddings tends to increase as domain shifts intensify in TTA. However, existing TTA methods often overlook this redundancy, which can hinder the model's adaptability to new data. To address this issue, we introduce Feature Redundancy Elimination for Test-time Adaptation (FRET), a novel perspective for TTA. A straightforward approach (S-FRET) is to directly minimize the feature redundancy score as an optimization objective to improve adaptation. Despite its simplicity and effectiveness, S-FRET struggles with label shifts, limiting its robustness in real-world scenarios. To mitigate this limitation, we further propose Graph-based FRET (G-FRET), which integrates a Graph Convolutional Network (GCN) with contrastive learning. This design not only reduces feature redundancy but also enhances feature discriminability in both the representation and prediction layers. Extensive experiments across multiple model architectures, tasks, and datasets demonstrate the effectiveness of S-FRET and show that G-FRET achieves state-of-the-art performance. Further analysis reveals that G-FRET enables the model to extract non-redundant and highly discriminative features during inference, thereby facilitating more robust test-time adaptation.

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