Joint Domain Alignment and Adversarial Learning for Domain Generalization
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0885-7_12
Reference19 articles.
1. Doan, K.D., Yang, P., Li, P.: One loss for quantization: deep hashing with discrete Wasserstein distributional matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9447–9457 (2022)
2. Chang, Q., et al.: Data: domain-aware and task-aware self-supervised learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9841–9850 (2022)
3. Zhang, Y., Li, M., Li, R., Jia, K., Zhang, L.: Exact feature distribution matching for arbitrary style transfer and domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8035–8045 (2022)
4. Zhang, X., He, Y., Xu, R., Yu, H., Shen, Z., Cui, P.: Nico++: towards better benchmarking for domain generalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16036–16047 (2023)
5. Rahman, M.M., Fookes, C., Baktashmotlagh, M., Sridharan, S.: Correlation-aware adversarial domain adaptation and generalization. Pattern Recogn. 100, 107124 (2019)
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