Source-Free Target Domain Confidence Calibration

Rotem Nizhar, Idit Diamant, Coby Penso, Jacob Goldberger

Keywords: confidence calibration, domain adaptation, source-free

TL;DR: We present a confidence calibration method for a source-free domain adaptation setup

Abstract:

In this study, we consider the setup of source-free domain adaptation and address the challenge of calibrating the confidence of a model adapted to the target domain using only unlabeled data. The primary challenge in addressing uncertainty calibration is the absence of labeled data which prevents computing the accuracy of the adapted network on the target domain. We address this by leveraging pseudo-labels generated from the source model’s predictions to estimate the true, unobserved accuracy. We demonstrate that, although the pseudo-labels are noisy, the network accuracy calculated using these pseudo-labels is similar to the accuracy obtained with the correct labels. We validate the effectiveness of our calibration approach by applying it to standard domain adaptation datasets and show that it achieves results comparable to, or even better than, previous calibration methods that relied on the availability of labeled source data.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 6363