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Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the robustness challenge under distribution shifts.
Statistical Learning Theory
Vapnik, V. N · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Generalizing from several related classification tasks to a new unlabeled sample
Blanchard, G., Lee, G., and Scott, C · 2011
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
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Learning to adapt structured output space for semantic segmentation
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M · 2018
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Group normalization
Wu, Y. and He, K · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2019
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Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
Cited alongside, same era.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Towards accurate model selection in deep unsupervised domain adaptation
You, K., Wang, X., Long, M., and Jordan, M · 2019
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Synthetic examples improve generalization for rare classes
Beery, S., Liu, Y., Morris, D., Piavis, J., Kapoor, A., Joshi, N., Meister, M., and Perona, P · 2020
Cited alongside, same era.
Model adaptation: Unsupervised domain adaptation without source data
Li, R., Jiao, Q., Cao, W., Wong, H.-S., and Wu, S · 2020
Cited alongside, same era.
Parameter-free online test-time adaptation
Boudiaf, M., Mueller, R., Ben Ayed, I., and Bertinetto, L · 2022
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Contrastive test-time adaptation
Chen, D., Wang, D., Darrell, T., and Ebrahimi, S · 2022
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Source-free adaptation to measurement shift via bottom-up feature restoration
Eastwood, C., Mason, I., Williams, C., and Schölkopf, B · 2022
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Back to the source: Diffusion-driven test-time adaptation
Gao, J., Zhang, J., Liu, X., Darrell, T., Shelhamer, E., and Wang, D · 2022
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Test-time adaptation via conjugate pseudo-labels
Goyal, S., Sun, M., Raghunathan, A., and Kolter, Z · 2022
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Pixmix: Dreamlike pictures comprehensively improve safety measures
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Liang, J., Hu, D., and Feng, J · 2020
Cited alongside, same era.
Improving robustness against common corruptions by covariate shift adaptation
Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., and Bethge, M · 2020
Cited alongside, same era.
Test-time training with self-supervision for generalization under distribution shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., and Hardt, M · 2020
Cited alongside, same era.
Limitations of post-hoc feature alignment for robustness
Burns, C. and Steinhardt, J · 2021
Cited alongside, same era.
Uncertainty reduction for model adaptation in semantic segmentation
Fleuret, F. et al · 2021
Cited alongside, same era.
In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2021
Cited alongside, same era.
Test-time classifier adjustment module for model-agnostic domain generalization
Iwasawa, Y. and Matsuo, Y · 2021
Cited alongside, same era.
Hendrycks, D., Zou, A., Mazeika, M., Tang, L., Li, B., Song, D., and Steinhardt, J · 2022
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Balancing discriminability and transferability for source-free domain adaptation
Kundu, J. N., Kulkarni, A. R., Bhambri, S., Mehta, D., Kulkarni, S. A., Jampani, V., and Radhakrishnan, V. B · 2022
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Confidence score for source-free unsupervised domain adaptation
Lee, J., Jung, D., Yim, J., and Yoon, S · 2022
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If your data distribution shifts, use self-learning
Rusak, E., Schneider, S., Pachitariu, G., Eck, L., Gehler, P. V., Bringmann, O., Brendel, W., and Bethge, M · 2022
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Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering
Su, Y., Xu, X., and Jia, K · 2022
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Beyond invariance: Test-time label-shift adaptation for distributions with" spurious" correlations
Sun, Q., Murphy, K., Ebrahimi, S., and D’Amour, A · 2022
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Continual test-time domain adaptation
Wang, Q., Fink, O., Van Gool, L., and Dai, D · 2022
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Attracting and dispersing: A simple approach for source-free domain adaptation
Yang, S., Wang, Y., Wang, K., Jui, S., et al · 2022
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Wild-time: A benchmark of in-the-wild distribution shift over time
Yao, H., Choi, C., Cao, B., Lee, Y., Koh, P. W., and Finn, C · 2022
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Test-time robust personalization for federated learning
Jiang, L. and Lin, T · 2023
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Towards stable test-time adaptation in dynamic wild world
Niu, S., Wu, J., Zhang, Y., Wen, Z., Chen, Y., Zhao, P., and Tan, M · 2023
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Test: Test-time self-training under distribution shift
Sinha, S., Gehler, P., Locatello, F., and Schiele, B · 2023
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