Fully Test-time Adaptation by Entropy Minimization
international conference on learning representations, 2020.
Faced with new and different data during testing, a model must adapt itself. We consider the setting of fully test-time adaptation, in which a supervised model confronts unlabeled test data from a different distribution, without the help of its labeled training data. We propose an entropy minimization approach for adaptation: we take th...More
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