Author:
Li Peizhao,Ding Zhengming,Liu Hongfu
Publisher
Springer Nature Singapore
Reference45 articles.
1. Balaji, Y., Chellappa, R., Feizi, S.: Normalized Wasserstein for mixture distributions with applications in adversarial learning and domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision (2019)
2. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Machine Learning (2010)
3. Lecture Notes in Computer Science;BB Damodaran,2018
4. Chen, Q., Liu, Y., Wang, Z., Wassell, I., Chetty, K.: Re-weighted adversarial adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
5. Chen, X., Wang, S., Long, M., Wang, J.: Transferability vs. discriminability: batch spectral penalization for adversarial domain adaptation. In: Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research (2019). http://proceedings.mlr.press/v97/chen19i.html