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Despite the huge effort in developing novel regularizers for Domain Generalization (DG), adding simple data augmentation to the vanilla ERM which is a practical implementation of the Vicinal Risk Minimization principle (VRM) \citep{chapelle2000vicinal} outperforms or stays competitive with many of the proposed regularizers.
Vicinal risk minimization
Chapelle, O., Weston, J., Bottou, L., and Vapnik, V · 2000
Earlier work this paper cites.
Generalizing from several related classification tasks to a new unlabeled sample
Blanchard, G., Lee, G., and Scott, C · 2011
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Fang, C., Xu, Y., and Rockmore, D. N · 2013
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2016
Earlier work this paper cites.
Deep CORAL: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
Earlier work this paper cites.
Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Earlier work this paper cites.
Does distributionally robust supervised learning give robust classifiers?
Hu, W., Niu, G., Sato, I., and Sugiyama, M · 2018
Earlier work this paper cites.
Domain generalization with adversarial feature learning
Li, H., Pan, S. J., Wang, S., and Kot, A. C · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Earlier work this paper cites.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Earlier work this paper cites.
Domain generalization by solving jigsaw puzzles
Carlucci, F. M., D’Innocente, A., Bucci, S., Caputo, B., and Tommasi, T · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
Foret, P., Kleiner, A., Mobahi, H., and Neyshabur, B · 2020
Cited alongside, same era.
Shahtalebi, S., Gagnon-Audet, J.-C., Laleh, T., Faramarzi, M., Ahuja, K., and Rish, I · 2021
Later among the works it cites.
Gradient matching for domain generalization
Shi, Y., Seely, J., Torr, P. H., Siddharth, N., Hannun, A., Usunier, N., and Synnaeve, G · 2021
Later among the works it cites.
Quantifying and improving transferability in domain generalization
Zhang, G., Zhao, H., Yu, Y., and Poupart, P · 2021
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Domain generalization with mixstyle
Zhou, K., Yang, Y., Qiao, Y., and Xiang, T · 2021
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Fishr: Invariant gradient variances for out-of-distribution generalization
Rame, A., Dancette, C., and Cord, M · 2022
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Gulrajani, I. and Lopez-Paz, D · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Out-of-distribution generalization with maximal invariant predictor
Koyama, M. and Yamaguchi, S · 2020
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Learning explanations that are hard to vary
Parascandolo, G., Neitz, A., Orvieto, A., Gresele, L., and Schölkopf, B · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
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Frustratingly simple domain generalization via image stylization
Somavarapu, N., Ma, C.-Y., and Kira, Z · 2020
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Domain generalization without excess empirical risk
Sener, O. and Koltun, V · 2022
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Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization
Ye, N., Li, K., Bai, H., Yu, R., Hong, L., Zhou, F., Li, Z., and Zhu, J · 2022
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Understanding hessian alignment for domain generalization
Hemati, S., Zhang, G., Estiri, A., and Chen, X · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al · 2023
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Image mixer, 2023
Pinkney, J · 2023
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Stablerep: Synthetic images from text-to-image models make strong visual representation learners
Tian, Y., Fan, L., Isola, P., Chang, H., and Krishnan, D · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L. and Agrawala, M · 2023
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