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Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference.
H. Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of Statistical Planning and Inference , vol. 90, no. 2, pp. 227–244, 2000
2000
Earlier work this paper cites.
S. Hochreiter, A. S. Younger, and P. R. Conwell, “Learning to learn using gradient descent,” in Proc. Int. Conf. on Artificial Neural Networks . Springer, 2001, pp. 87–94
2001
Earlier work this paper cites.
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf, “Covariate shift by kernel mean matching,” in Dataset Shift in Machine Learning , J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, Eds. MIT Press, 2008, pp. 131–160
2008
Earlier work this paper cites.
G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” Pattern Recognition Letters , vol. 30, no. 2, pp. 88–97, 2009
2009
Earlier work this paper cites.
H. Daumé III, “Frustratingly easy domain adaptation,” arXiv preprint arXiv:0907.1815 , 2009
2009
Earlier work this paper cites.
S. Shalev-Shwartz et al. , “Online learning and online convex optimization,” Foundations and Trends in Machine Learning , vol. 4, no. 2, pp. 107–194, 2011
2011
Earlier work this paper cites.
D. Lazer, R. Kennedy, G. King, and A. Vespignani, “The parable of Google Flu: traps in big data analysis,” Science , vol. 343, no. 6176, pp. 1203–1205, 2014
2014
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in Proc. Int. Conf. Machine Learning . PMLR, 2015, pp. 1180–1189
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta-learning with memory-augmented neural networks,” in Proc. Int. Conf. Machine Learning . PMLR, 2016, pp. 1842–1850
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” Advances in Neural Information Processing Systems , vol. 29, pp. 3630–3638, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. Int. Conf. Machine Learning . PMLR, 2017, pp. 1126–1135
2017
Cited alongside, same era.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in Proc. Int. Conf. Learning Representations , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
B. Sun, J. Feng, and K. Saenko, “Correlation alignment for unsupervised domain adaptation,” in Domain Adaptation in Computer Vision Applications , G. Csurka, Ed. Springer, 2017, pp. 153–171
2017
Cited alongside, same era.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2017, pp. 7167–7176
2019
Later among the works it cites.
H. Zhao, “Semseg,” https://github.com/hszhao/semseg
2019
Later among the works it cites.
J. N. Kundu, N. Venkat, R. V. Babu et al. , “Universal source-free domain adaptation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2020, pp. 4544–4553
2020
Later among the works it cites.
R. Li, Q. Jiao, W. Cao, H.-S. Wong, and S. Wu, “Model adaptation: Unsupervised domain adaptation without source data,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2020, pp. 9641–9650
2020
Later among the works it cites.
J. Liang, D. Hu, and J. Feng, “Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,” in Proc. Int. Conf. Machine Learning . PMLR, 2020, pp. 6028–6039
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Learning to generalize: Meta-learning for domain generalization,” in Proc. AAAI Conf. on Artificial Intelligence , 2018, pp. 3490–3497
2018
Cited alongside, same era.
Z. Lipton, Y.-X. Wang, and A. Smola, “Detecting and correcting for label shift with black box predictors,” in Proc. Int. Conf. Machine Learning . PMLR, 2018, pp. 3122–3130
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Dou, D. Coelho de Castro, K. Kamnitsas, and B. Glocker, “Domain generalization via model-agnostic learning of semantic features,” Advances in Neural Information Processing Systems , vol. 32, pp. 6450–6461, 2019
2019
Cited alongside, same era.
E. Hazan, “Introduction to online convex optimization,” arXiv preprint arXiv:1909.05207 , 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
Y. Sun, X. Wang, Z. Liu, J. Miller, A. Efros, and M. Hardt, “Test-time training with self-supervision for generalization under distribution shifts,” in Proc. Int. Conf. Machine Learning . PMLR, 2020, pp. 9229–9248
2020
Later among the works it cites.
2020
Later among the works it cites.
C. Wu, Y. Chen, J. Luo, C.-C. Su, A. Dawane, B. Hanzra, Z. Deng, B. Liu, J. Z. Wang, and C.-h. Kuo, “MEBOW: Monocular estimation of body orientation in the wild,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2020, pp. 3451–3461
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Zhang, “ARM,” https://github.com/henrikmarklund/arm
2021
Later among the works it cites.
M. Zhang, H. Marklund, N. Dhawan, A. Gupta, S. Levine, and C. Finn, “Adaptive risk minimization: Learning to adapt to domain shift,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Later among the works it cites.
T. Gong, J. Jeong, T. Kim, Y. Kim, J. Shin, and S.-J. Lee, “NOTE: Robust continual test-time adaptation against temporal correlation,” in Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
Q. Wang, O. Fink, L. Van Gool, and D. Dai, “Continual test-time domain adaptation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2022, pp. 7201–7211
2022
Later among the works it cites.
B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2012, pp. 2066–2073
2073
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