Parameter-free Online Test-time Adaptation

  title={Parameter-free Online Test-time Adaptation},
  author={Malik Boudiaf and Romain Mueller and Ismail Ben Ayed and Luca Bertinetto},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
Training state-of-the-art vision models has become prohibitively expensive for researchers and practitioners. For the sake of accessibility and resource reuse, it is important to focus on adapting these models to a variety of down-stream scenarios. An interesting and practical paradigm is online test-time adaptation, according to which training data is inaccessible, no labelled data from the test distribution is available, and adaptation can only happen at test time and on a handful of samples… 

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