Test-time adaptation (TTA) refers to adapting neural networks to distribution
shifts, with access to only the unlabeled test samples from the new domain at
test-time. Prior TTA methods optimize over unsupervised objectives such as the
entropy of model predictions in TENT [Wang et al., 2021], but it is unclear
what exactly makes a good TTA loss. In this paper, we start by presenting a
surprising phenomenon: if we attempt to meta-learn the best possible TTA loss
over a wide class of functions, then we recover a function that is remarkably
similar to (a temperature-scaled version of) the softmax-entropy employed by
TENT. This only holds, however, if the classifier we are adapting is trained
via cross-entropy; if trained via squared loss, a different best TTA loss
emerges. To explain this phenomenon, we analyze TTA through the lens of the
training losses's convex conjugate. We show that under natural conditions, this
(unsupervised) conjugate function can be viewed as a good local approximation
to the original supervised loss and indeed, it recovers the best losses found
by meta-learning. This leads to a generic recipe that can be used to find a
good TTA loss for any given supervised training loss function of a general
class. Empirically, our approach consistently dominates other baselines over a
wide range of benchmarks. Our approach is particularly of interest when applied
to classifiers trained with novel loss functions, e.g., the recently-proposed
PolyLoss, where it differs substantially from (and outperforms) an
entropy-based loss. Further, we show that our approach can also be interpreted
as a kind of self-training using a very specific soft label, which we refer to
as the conjugate pseudolabel. Overall, our method provides a broad framework
for better understanding and improving test-time adaptation. Code is available
at this https URL

本文研究了针对分布漂移进行测试时间自适应（TTA）的方法，在各种函数类中元学习 TTA loss 能够迅速获得与熵函数类似的 loss 函数，并基于最优 loss 函数提供了更好的 TTA 方法。同时，在新型的 supervised training loss 函数中，我们的方法也显示了优异的表现，为改善测试时间自适应提供了广阔的框架。