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Recently, test time adaptation (TTA) has attracted increasing attention due to its power of handling the distribution shift issue in the real world.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. et al · 2013
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Revisiting batch normalization for practical domain adaptation
Li, Y., Wang, N., Shi, J., Liu, J., and Hou, X · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
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On mutual information maximization for representation learning
Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., and Lucic, M · 2019
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Confidence regularized self-training
Zou, Y., Yu, Z., Liu, X., Kumar, B., and Wang, J · 2019
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A., Lyzhov, A., Molchanov, D., and Vetrov, D · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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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.
Evaluating prediction-time batch normalization for robustness under covariate shift
Nado, Z., Padhy, S., Sculley, D., D’Amour, A., Lakshminarayanan, B., and Snoek, 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.
Contrastive multiview coding
Tian, Y., Krishnan, D., and Isola, P · 2020
Cited alongside, same era.
Adversarial graph augmentation to improve graph contrastive learning
Suresh, S., Li, P., Hao, C., and Neville, J · 2021
Later among the works it cites.
Memo: Test time robustness via adaptation and augmentation
Zhang, M., Levine, S., and Finn, C · 2021
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Contrastive test-time adaptation
Chen, D., Wang, D., Darrell, T., and Ebrahimi, S · 2022
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Softedge: Regularizing graph classification with random soft edges
Guo, H. and Sun, S · 2022
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G-mixup: Graph data augmentation for graph classification
Han, X., Jiang, Z., Liu, N., and Hu, X · 2022
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Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2020
Cited alongside, same era.
Graph information bottleneck
Wu, T., Ren, H., Li, P., and Leskovec, J · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Cited alongside, same era.
Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2020
Cited alongside, same era.
A closer look at distribution shifts and out-of-distribution generalization on graphs
Ding, M., Kong, K., Chen, J., Kirchenbauer, J., Goldblum, M., Wipf, D., Huang, F., and Goldstein, T · 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.
Ttt++: When does self-supervised test-time training fail or thrive?
Liu, Y., Kothari, P., van Delft, B., Bellot-Gurlet, B., Mordan, T., and Alahi, A · 2021
Cited alongside, same era.
Ji, Y., Zhang, L., and et al · 2022
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Out-of-distribution generalization on graphs: A survey
Li, H., Wang, X., Zhang, Z., and Zhu, W · 2022
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Automated data augmentations for graph classification
Luo, Y., McThrow, M., Au, W. Y., Komikado, T., Uchino, K., Maruhash, K., and Ji, S · 2022
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Efficient test-time model adaptation without forgetting
Niu, S., Wu, J., Zhang, Y., Chen, Y., Zheng, S., Zhao, P., and Tan, M · 2022
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Learnable hypergraph laplacian for hypergraph learning
Zhang, J., Chen, Y., Xiao, X., Lu, R., and Xia, S.-T · 2022
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Graph data augmentation for graph machine learning: A survey
Zhao, T., Liu, G., Günnemann, S., and Jiang, M · 2022
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