Test Time Adaptation via Conjugate Pseudo-labelsDownload PDF

Published: 31 Oct 2022, Last Modified: 06 Jan 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: Test Time Adaptation, Domain Adaptation
TL;DR: We provide a generic framework for designing test-time adaptation loss for neural-networks trained using various loss functions like cross-entropy, polyloss and squared loss.
Abstract: Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, specifically with just access to 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 $\textit{meta-learn}$ the ``best'' possible TTA loss over a wide class of functions, then we recover a function that is $\textit{remarkably}$ 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 loss; if the classifier is trained via squared loss, a different ``best'' TTA loss emerges. To explain this phenomenon, we analyze test-time adaptation through the lens of the training losses's $\textit{convex conjugate}$. 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 than be used to find a good TTA loss for $\textit{any}$ given supervised training loss function of a general class. Empirically, our approach dominates other TTA alternatives over a wide range of domain adaptation benchmarks. Our approach is particularly of interest when applied to classifiers trained with $\textit{novel}$ loss functions, e.g., the recently-proposed PolyLoss (Leng et al., 2022) function, where it differs substantially from (and outperforms) an entropy-based loss. Further, we show that our conjugate based approach can also be interpreted as a kind of self-training using a very specific soft label, which we refer to as the $\textit{conjugate pseudo-label}$. Overall, therefore, our method provides a broad framework for better understanding and improving test-time adaptation. Code is available at https://github.com/locuslab/tta_conjugate.
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