  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.
