Enhanced Online Test-time Adaptation with Feature-Weight Cosine Alignment
This addresses distributional shifts in real-time model adaptation for computer vision applications, representing a strong incremental improvement over existing OTTA methods.
The paper tackles the problem of noisy gradients and incorrect predictions in Online Test-Time Adaptation (OTTA) by introducing a cosine alignment optimization method with a dual-objective loss function. The method outperforms state-of-the-art techniques, achieving high accuracy and robustness across multiple datasets like CIFAR-10-C, CIFAR-100-C, ImageNet-C, Office-Home, and DomainNet.
Online Test-Time Adaptation (OTTA) has emerged as an effective strategy to handle distributional shifts, allowing on-the-fly adaptation of pre-trained models to new target domains during inference, without the need for source data. We uncovered that the widely studied entropy minimization (EM) method for OTTA, suffers from noisy gradients due to ambiguity near decision boundaries and incorrect low-entropy predictions. To overcome these limitations, this paper introduces a novel cosine alignment optimization approach with a dual-objective loss function that refines the precision of class predictions and adaptability to novel domains. Specifically, our method optimizes the cosine similarity between feature vectors and class weight vectors, enhancing the precision of class predictions and the model's adaptability to novel domains. Our method outperforms state-of-the-art techniques and sets a new benchmark in multiple datasets, including CIFAR-10-C, CIFAR-100-C, ImageNet-C, Office-Home, and DomainNet datasets, demonstrating high accuracy and robustness against diverse corruptions and domain shifts.