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In domain generalization (DG), the target domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) target domain during inference.
An overview of statistical learning theory
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., and Li, S · 2008
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Multi-camera activity correlation analysis
Loy, C. C., Xiang, T., and Gong, S · 2009
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Fang, C., Xu, Y., and Rockmore, D. N · 2013
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Scalable person re-identification: A benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q · 2015
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Robust domain generalisation by enforcing distribution invariance
Erfani, S., Baktashmotlagh, M., Moshtaghi, M., Nguyen, X., Leckie, C., Bailey, J., and Kotagiri, R · 2016
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Domain-adversarial training of neural networks
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Learning deep representation for imbalanced classification
Huang, C., Li, Y., Loy, C. C., and Tang, X · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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Domain generalization by marginal transfer learning
Blanchard, G., Deshmukh, A. A., Dogan, U., Lee, G., and Scott, C · 2017
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A learned representation for artistic style
Dumoulin, V., Shlens, J., and Kudlur, M · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S · 2017
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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Dynamic sampling in convolutional neural networks for imbalanced data classification
Pouyanfar, S., Tao, Y., Mohan, A., Tian, H., Kaseb, A. S., Gauen, K., Dailey, R., Aghajanzadeh, S., Lu, Y.-H., Chen, S.-C., et al · 2018
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Generalizing across domains via cross-gradient training
Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., and Sarawagi, S · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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Episodic training for domain generalization
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z., and Hospedales, T. M · 2019
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Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2021
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Test-time classifier adjustment module for model-agnostic domain generalization
Iwasawa, Y. and Matsuo, Y · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., and Courville, A · 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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Reducing domain gap by reducing style bias
Nam, H., Lee, H., Park, J., Yoon, W., and Yoo, D · 2021
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Permuted adain: reducing the bias towards global statistics in image classification
Nuriel, O., Benaim, S., and Wolf, L · 2021
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Shu, J., Xie, Q., Yi, L., Zhao, Q., Zhou, S., Xu, Z., and Meng, D · 2019
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Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., and Gong, B · 2019
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Omni-scale feature learning for person re-identification
Zhou, K., Yang, Y., Cavallaro, A., and Xiang, T · 2019
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Learning to learn with variational information bottleneck for domain generalization
Du, Y., Xu, J., Xiong, H., Qiu, Q., Zhen, X., Snoek, C. G., and Shao, L · 2020
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Self-challenging improves cross-domain generalization
Huang, Z., Wang, H., Xing, E. P., and Huang, D · 2020
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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
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Generalization on unseen domains via inference-time label-preserving target projections
Pandey, P., Raman, M., Varambally, S., and Ap, P · 2021
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Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification
Zhao, Y., Zhong, Z., Yang, F., Luo, Z., Lin, Y., Li, S., and Sebe, N · 2021
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Domain generalization with mixstyle
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Compound domain generalization via meta-knowledge encoding
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Style neophile: Constantly seeking novel styles for domain generalization
Kang, J., Lee, S., Kim, N., and Kwak, S · 2022
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Uncertainty modeling for out-of-distribution generalization
Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., and Duan, L · 2022
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Learning to generalize across domains on single test samples
Xiao, Z., Zhen, X., Shao, L., and Snoek, C. G · 2022
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On multi-domain long-tailed recognition, generalization and beyond
Yang, Y., Wang, H., and Katabi, D · 2022
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Exact feature distribution matching for arbitrary style transfer and domain generalization
Zhang, Y., Li, M., Li, R., Jia, K., and Zhang, L · 2022
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Test-time fourier style calibration for domain generalization
Zhao, X., Liu, C., Sicilia, A., Hwang, S. J., and Fu, Y · 2022
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