CONF Prabhu_Distshift2021/IDIAP Test time Adaptation through Perturbation Robustness Sivaprasad, Prabhu Teja Fleuret, Francois EXTERNAL https://publications.idiap.ch/attachments/papers/2021/Prabhu_Distshift2021.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Sivaprasad_Idiap-RR-17-2021 Related documents Workshop on Distribution Shifts, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) 2021 Data samples generated by several real world processes are dynamic in nature 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 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. REPORT Sivaprasad_Idiap-RR-17-2021/IDIAP Test time Adaptation through Perturbation Robustness Sivaprasad, Prabhu Teja Fleuret, Francois Idiap-RR-17-2021 2021 Idiap November 2021 Data samples generated by several real world processes are dynamic in nature 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 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.