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This document presents a new test-time adaptation (TTA) method called NOTE, which addresses the challenges of adapting deep neural networks to temporally correlated (non-i.i.d.) test data streams. The method incorporates Instance-Aware Batch Normalization (IABN) and Prediction-balanced Reservoir Sampling (PBRS) to improve model performance under non-i.i.d. conditions, showing significant error rate reductions compared to existing TTA algorithms. Evaluations on various datasets indicate that NOTE not only ex

Author

trssa403

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EN