This paper proposes a novel batch normalization strategy for test-time
adaptation. Recent test-time adaptation methods heavily rely on the modified
batch normalization, i.e., transductive batch normalization (TBN), which
calculates the mean and the variance from the current test batch rather than
using the running mean and variance obtained from the source data, i.e.,
conventional batch normalization (CBN). Adopting TBN that employs test batch
statistics mitigates the performance degradation caused by the domain shift.
However, re-estimating normalization statistics using test data depends on
impractical assumptions that a test batch should be large enough and be drawn
from i.i.d. stream, and we observed that the previous methods with TBN show
critical performance drop without the assumptions. In this paper, we identify
that CBN and TBN are in a trade-off relationship and present a new test-time
normalization (TTN) method that interpolates the statistics by adjusting the
importance between CBN and TBN according to the domain-shift sensitivity of
each BN layer. Our proposed TTN improves model robustness to shifted domains
across a wide range of batch sizes and in various realistic evaluation
scenarios. TTN is widely applicable to other test-time adaptation methods that
rely on updating model parameters via backpropagation. We demonstrate that
adopting TTN further improves their performance and achieves state-of-the-art
performance in various standard benchmarks.

本文提出了一种新型的批次规范化策略 —— 测试时间适应的归一化统计方法，通过调整传统归一化和传输归一化统计信息的权重，以适应各种实际评估场景，提高模型对于不同批次大小和领域转移的鲁棒性，实现了各项标准评测中的最佳性能。

TTN：测试时间适应中考虑领域变化的批量归一化

TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation

In Test-time Adaptation (TTA), given a source model, the goal is to adapt it
to make better predictions for test instances from a different distribution
than the source. Crucially, TTA assumes no access to the source data or even
any additional labeled/unlabeled samples from the target distribution to
finetune the source model. In this work, we consider TTA in a more pragmatic
setting which we refer to as SITA (Single Image Test-time Adaptation). Here,
when making a prediction, the model has access only to the given single test
instance, rather than a batch of instances, as typically been considered in the
literature. This is motivated by the realistic scenarios where inference is
needed on-demand instead of delaying for an incoming batch or the inference is
happening on an edge device (like mobile phone) where there is no scope for
batching. The entire adaptation process in SITA should be extremely fast as it
happens at inference time. To address this, we propose a novel approach AugBN
that requires only a single forward pass. It can be used on any off-the-shelf
trained model to test single instances for both classification and segmentation
tasks. AugBN estimates normalization statistics of the unseen test distribution
from the given test image using only one forward pass with label-preserving
transformations. Since AugBN does not involve any back-propagation, it is
significantly faster compared to recent test time adaptation methods. We
further extend AugBN to make the algorithm hyperparameter-free. Rigorous
experimentation show that our simple algorithm is able to achieve significant
performance gains for a variety of datasets, tasks, and network architectures.

该论文主要研究了用于单镜头图像推理的快速测试时间自适应方法（SITA）。 提出了一种名为 AugBN 的方法，它只需要一次前向传递来估计未见过的测试分布的规范化统计，该方法无需反向传播并且是无超参数的。 在实验中，该算法在各种数据集、任务和网络架构上都取得了显著的性能提升。