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Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data.
The organization of behavior
Hebb, D. O. 1988 · 1988
Earlier work this paper cites.
The role of constraints in Hebbian learning
Miller, K. D.; and MacKay, D. J. C. 1994 · 1994
Earlier work this paper cites.
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
Song, S.; Miller, K. D.; and Abbott, L. F. 2000 · 2000
Earlier work this paper cites.
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 · 2006
Earlier work this paper cites.
Improving robustness against common corruptions by covariate shift adaptation
Schneider, S.; Rusak, E.; Eck, L.; Bringmann, O.; Brendel, W.; and Bethge, M. 2020 · 2006
Earlier work this paper cites.
Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
Lee, D.-H. 2013 · 2013
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
OctoMiao. 2016 · 2016
Earlier work this paper cites.
Wide Residual Networks
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
Earlier work this paper cites.
Continual Learning Through Synaptic Intelligence
Zenke, F.; Poole, B.; and Ganguli, S. 2017 · 2017
Earlier work this paper cites.
Adaptive Batch Normalization for practical domain adaptation
Li, Y.; Wang, N.; Shi, J.; Hou, X.; and Liu, J. 2018 · 2018
Earlier work this paper cites.
Experience Replay for Continual Learning
Rolnick, D.; Ahuja, A.; Schwarz, J.; Lillicrap, T. P.; and Wayne, G. 2018 · 2018
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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
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Cited alongside, same era.
RobustBench: a standardized adversarial robustness benchmark
Croce, F.; Andriushchenko, M.; Sehwag, V.; Debenedetti, E.; Flammarion, N.; Chiang, M.; Mittal, P.; and Hein, M. 2020 · 2020
Cited alongside, same era.
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Liang, J.; Hu, D.; and Feng, J. 2020 · 2020
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Limitations of Post-Hoc Feature Alignment for Robustness
Burns, C.; and Steinhardt, J. 2021 · 2021
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A continual learning survey: Defying forgetting in classification tasks
Generalized Source-Free Domain Adaptation
Yang, S.; Wang, Y.; van de Weijer, J.; Herranz, L.; and Jui, S. 2021 · 2021
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Exploring Visual Prompts for Adapting Large-Scale Models
Bahng, H.; Jahanian, A.; Sankaranarayanan, S.; and Isola, P. 2022 · 2022
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Parameter-free Online Test-time Adaptation
Boudiaf, M.; Mueller, R.; Ayed, I. B.; and Bertinetto, L. 2022 · 2022
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Contrastive Test-Time Adaptation
Chen, D.; Wang, D.; Darrell, T.; and Ebrahimi, S. 2022 · 2022
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Domain Adaptation via Prompt Learning
Ge, C.; Huang, R.; Xie, M.; Lai, Z.; Song, S.; Li, S.; and Huang, G. 2022 · 2022
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Align and Prompt: Video-and-Language Pre-training with Entity Prompts
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Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; and Neubig, G. 2021 · 2021
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Feature Importance-aware Transferable Adversarial Attacks
Wang, Z.; Guo, H.; Zhang, Z.; Liu, W.; Qin, Z.; and Ren, K. 2021b · 2021
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Tent: Fully Test-Time Adaptation by Entropy Minimization
Wang, D.; Shelhamer, E.; Liu, S.; Olshausen, B. A.; and Darrell, T. 2021a
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Continual Test-Time Domain Adaptation
Wang, Q.; Fink, O.; Gool, L. V.; and Dai, D. 2022a
Cited in the paper.
Learning to Prompt for Continual Learning
Wang, Z.; Zhang, Z.; Lee, C.-Y.; Zhang, H.; Sun, R.; Ren, X.; Su, G.; Perot, V.; Dy, J.; and Pfister, T. 2022b
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Visual Prompt Tuning
Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022 · 2022
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Real-World Robot Learning with Masked Visual Pre-training
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Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Sharma, A.; Bian, Y.; Munz, P.; and Narayan, A. 2022 · 2022
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Multitask Vision-Language Prompt Tuning
Shen, S.; Yang, S.; Zhang, T.; Zhai, B.; Gonzalez, J. E.; Keutzer, K.; and Darrell, T. 2022 · 2022
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