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
this https URL

测试时间适应 (TTA) 目的在于在部署后无法访问源数据的情况下，将预训练模型调整到新的测试领域。我们提出了 DPLOT，一个简单而有效的 TTA 框架，主要包括两个组件：(1) 选择涉及特定领域特征提取的块，通过熵最小化对这些块进行训练；(2) 使用配对视图图像生成伪标签。通过简单地使用翻转增强，我们防止了伪标签质量的降低。我们的实验结果表明，DPLOT 在 CIFAR10-C、CIFAR100-C 和 ImageNet-C 基准测试中表现优于以前的 TTA 方法，分别减少了 5.4%、9.1% 和 2.9% 的错误。