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Test Time Robustification of Deep Models via Adaptation and Augmentation
We study the problem of test time robustification, i. e., using the test input to improve model robustness.
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.
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