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| GAN thích ứng miền× | GAN bán giám sát× | |
|---|---|---|
| Lĩnh vực | Học sâu | Học sâu |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2016–2017 | 2016 |
| Người khởi xướng≠ | Ganin et al. (DANN); Zhu et al. (CycleGAN) | Odena, A.; Salimans, T. et al. |
| Loại≠ | Generative adversarial model with domain adaptation | Semi-supervised generative model |
| Công trình gốc≠ | Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ | Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved Techniques for Training GANs. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| Tên gọi khác | DA-GAN, domain adaptation GAN, adversarial domain adaptation, domain-adaptive generative adversarial network | SGAN, Semi-GAN, semi-supervised generative adversarial network, GAN-based semi-supervised learning |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | A Domain-Adaptive GAN combines generative adversarial learning with domain adaptation to bridge the distribution gap between a labeled source domain and an unlabeled or sparsely labeled target domain. By training a generator and discriminator adversarially, the model learns domain-invariant representations or translated samples, enabling a classifier or detector trained on source data to generalize effectively to the target domain without requiring abundant target labels. | Semi-supervised GAN (SGAN) extends the standard GAN discriminator to simultaneously classify labeled examples into K real classes and detect generated fakes as a (K+1)-th class, letting the generator's synthetic data act as implicit regularization and allowing strong classifiers to be trained with very few labeled examples. |
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