Network calibration under domain shift based on estimating the target domain accuracy
Primary Area: transfer learning, meta learning, and lifelong learning
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Keywords: domain shift, confidence calibration, unsupervised domain adaptation
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TL;DR: We propose a confidence calibration method for unsupervised domain adaptation which is done directly on the target domain.
Abstract: In this study, we address the problem of calibrating network confidence while adapting a model that was originally trained on a source domain to a target domain using unlabeled samples from the target domain. The absence of labels from the target domain makes it impossible to directly calibrate the adapted network on the target domain. To tackle this challenge, we introduce a calibration procedure that relies on estimating the network's accuracy on the target domain. The network accuracy is first computed on the labeled source data and then is modified to represent the actual accuracy of the model on the target domain. The proposed algorithm calibrates the prediction confidence directly in the target domain by minimizing the disparity between the estimated accuracy and the computed confidence. The experimental results show that our method significantly outperforms existing methods, which rely on importance weighting, across several standard datasets.
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Submission Number: 1476
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