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• W3207435585 abstract "Data samples generated by several real world processes are dynamic in nature textit{i.e.}, their characteristics vary with time. Thus it is not possible to train and tackle all possible distributional shifts between training and inference, using the host of transfer learning methods in literature. In this paper, we tackle this problem of adapting to domain shift at inference time textit{i.e.}, we do not change the training process, but quickly adapt the model at test-time to handle any domain shift. For this, we propose to enforce consistency of predictions of data sampled in the vicinity of test sample on the image manifold. On a host of test scenarios like dealing with corruptions (CIFAR-10-C and CIFAR-100-C), and domain adaptation (VisDA-C), our method is at par or significantly outperforms previous methods." @default.
• W3207435585 created "2021-10-25" @default.
• W3207435585 creator A5066773304 @default.
• W3207435585 creator A5076094010 @default.
• W3207435585 date "2021-10-19" @default.
• W3207435585 modified "2023-10-04" @default.
• W3207435585 title "Test time Adaptation through Perturbation Robustness." @default.
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• W3207435585 hasPublicationYear "2021" @default.
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