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Continual Test Time Adaptation (CTTA) is required to adapt efficiently to continuous unseen domains while retaining previously learned knowledge.
Evaluating prediction-time batch normalization for robustness under covariate shift
Nado, Z., Padhy, S., Sculley, D., D’Amour, A., Lakshminarayanan, B., and Snoek, J · 2006
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 · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Richter, S. R., Vineet, V., Roth, S., and Koltun, V · 2016
Earlier work this paper cites.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A. M · 2016
Earlier work this paper cites.
The mapillary vistas dataset for semantic understanding of street scenes
Neuhold, G., Ollmann, T., Rota Bulo, S., and Kontschieder, P · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J · 2017
Earlier work this paper cites.
Gradient based sample selection for online continual learning
Aljundi, R., Lin, M., Goujaud, B., and Bengio, Y · 2019
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Tsit: A simple and versatile framework for image-to-image translation
Jiang, L., Zhang, C., Huang, M., Liu, C., Shi, J., and Loy, C. C · 2020
Earlier work this paper cites.
Tent: Fully test-time adaptation by entropy minimization
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2020
Cited alongside, same era.
Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T · 2020
Cited alongside, same era.
Rainbow memory: Continual learning with a memory of diverse samples
Bang, J., Kim, H., Yoo, Y., Ha, J.-W., and Choi, J · 2021
Cited alongside, same era.
Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening
Choi, S., Jung, S., Yun, H., Kim, J. T., Kim, S., and Choo, J · 2021
Cited alongside, same era.
Online continual learning on class incremental blurry task configuration with anytime inference
Koh, H., Kim, D., Ha, J.-W., and Choi, J · 2021
Cited alongside, same era.
Visual prompt tuning for test-time domain adaptation
Gao, Y., Shi, X., Zhu, Y., Wang, H., Tang, Z., Zhou, X., Li, M., and Metaxas, D. N · 2022
Later among the works it cites.
Robust continual test-time adaptation: Instance-aware bn and prediction-balanced memory
Gong, T., Jeong, J., Kim, T., Kim, Y., Shin, J., and Lee, S.-J · 2022
Later among the works it cites.
Efficient test-time model adaptation without forgetting
Niu, S., Wu, J., Zhang, Y., Chen, Y., Zheng, S., Zhao, P., and Tan, M · 2022
Later among the works it cites.
Meta-dmoe: Adapting to domain shift by meta-distillation from mixture-of-experts
Zhong, T., Chi, Z., Gu, L., Wang, Y., Yu, Y., and Tang, J · 2022
Later among the works it cites.
Decorate the newcomers: Visual domain prompt for continual test time adaptation
Gan, Y., Bai, Y., Lou, Y., Ma, X., Zhang, R., Shi, N., and Luo, L · 2023
Later among the works it cites.
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Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding
Sakaridis, C., Dai, D., and Van Gool, L · 2021
Cited alongside, same era.
Segformer: Simple and efficient design for semantic segmentation with transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., and Luo, P · 2021
Cited alongside, same era.
Taming sparsely activated transformer with stochastic experts
Zuo, S., Liu, X., Jiao, J., Kim, Y. J., Hassan, H., Zhang, R., Zhao, T., and Gao, J · 2021
Cited alongside, same era.
Covariance-aware feature alignment with pre-computed source statistics for test-time adaptation
Adachi, K., Yamaguchi, S., and Kumagai, A · 2022
Cited alongside, same era.
Online continual learning on a contaminated data stream with blurry task boundaries
Bang, J., Koh, H., Park, S., Song, H., Ha, J.-W., and Choi, J · 2022
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
Fedus, W., Zoph, B., and Shazeer, N · 2022
Cited alongside, same era.
Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes
Choi, S., Yang, S., Choi, S., and Yun, S
Cited in the paper.
Cafa: Class-aware feature alignment for test-time adaptation
Jung, S., Lee, J., Kim, N., Shaban, A., Boots, B., and Choo, J · 2023
Later among the works it cites.
Ttn: A domain-shift aware batch normalization in test-time adaptation
Lim, H., Kim, B., Choo, J., and Choi, S · 2023
Later among the works it cites.
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
Later among the works it cites.
Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
Song, J., Lee, J., Kweon, I. S., and Choi, S · 2023
Later among the works it cites.
Albumentations: Fast and flexible image augmentations
Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A · 2078
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