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。