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Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t.
Covid-da: deep domain adaptation from typical pneumonia to covid-19
Zhang, Y., Niu, S., Qiu, Z., Wei, Y., Zhao, P., Yao, J., Huang, J., Wu, Q., and Tan, M · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Learning without forgetting
Li, Z. and Hoiem, D · 2017
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J · 2018
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Multi-adversarial domain adaptation
Pei, Z., Cao, Z., Long, M., and Wang, J · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T · 2018
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Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Fast autoaugment
Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S · 2019
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Experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., and Wayne, G · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 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. P · 2020
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Inf-net: Automatic covid-19 lung infection segmentation from ct images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., and Shao, L · 2020
Cited alongside, same era.
Orthogonal gradient descent for continual learning
Farajtabar, M., Azizan, N., Mott, A., and Li, A · 2020
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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 · 2020
Cited alongside, same era.
Universal source-free domain adaptation
Kundu, J. N., Venkat, N., Babu, R. V., et al · 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.
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Test-time classifier adjustment module for model-agnostic domain generalization
Iwasawa, Y. and Matsuo, Y · 2021
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Sita: Single image test-time adaptation
Khurana, A., Paul, S., Rai, P., Biswas, S., and Aggarwal, G · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al · 2021
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On feature normalization and data augmentation
Li, B., Wu, F., Lim, S.-N., Belongie, S., and Weinberger, K. Q · 2021
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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
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Liang, J., Hu, D., and Feng, J · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Liu, W., Wang, X., Owens, J., and Li, Y · 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.
A simple way to make neural networks robust against diverse image corruptions
Rusak, E., Schott, L., Zimmermann, R. S., Bitterwolf, J., Bringmann, O., Bethge, M., and Brendel, W · 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.
Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
Cited alongside, same era.
Madaan, D., Shin, J., and Hwang, S. J · 2021
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Essentials for class incremental learning
Mittal, S., Galesso, S., and Brox, T · 2021
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Disturbance-immune weight sharing for neural architecture search
Niu, S., Wu, J., Zhang, Y., Guo, Y., Zhao, P., Huang, J., and Tan, M · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation
Qiu, Z., Zhang, Y., Lin, H., Niu, S., Liu, Y., Du, Q., and Tan, M · 2021
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Tent: Fully test-time adaptation by entropy minimization
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2021
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Towards accurate text-based image captioning with content diversity exploration
Xu, G., Niu, S., Tan, M., Luo, Y., Du, Q., and Wu, Q · 2021
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Memo: Test time robustness via adaptation and augmentation
Zhang, M. M., Levine, S., and Finn, C · 2021
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Improving corruption and adversarial robustness by enhancing weak subnets
Guo, Y., Stutz, D., and Schiele, B · 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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Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
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