  Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test
domain without access to source data after deployment. Existing approaches
typically rely on self-training with pseudo-labels since ground-truth cannot be
obtained from test data. Although the quality of pseudo labels is important for
stable and accurate long-term adaptation, it has not been previously addressed.
In this work, we propose DPLOT, a simple yet effective TTA framework that
consists of two components: (1) domain-specific block selection and (2)
pseudo-label generation using paired-view images. Specifically, we select
blocks that involve domain-specific feature extraction and train these blocks
by entropy minimization. After blocks are adjusted for current test domain, we
generate pseudo-labels by averaging given test images and corresponding flipped
counterparts. By simply using flip augmentation, we prevent a decrease in the
quality of the pseudo-labels, which can be caused by the domain gap resulting
from strong augmentation. Our experimental results demonstrate that DPLOT
outperforms previous TTA methods in CIFAR10-C, CIFAR100-C, and ImageNet-C
benchmarks, reducing error by up to 5.4%, 9.1%, and 2.9%, respectively. Also,
we provide an extensive analysis to demonstrate effectiveness of our framework.
Code is available at
https://github.com/gist-ailab/domain-specific-block-selection-and-paired-view-pseudo-labeling-for-online-TTA.
