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| Self-supervised GAN× | 준지도 학습 GAN× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019 | 2016 |
| 창시자≠ | Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. | Odena, A.; Salimans, T. et al. |
| 유형≠ | Generative model with self-supervised auxiliary tasks | Semi-supervised generative model |
| 원전≠ | Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019). Self-Supervised GANs via Auxiliary Rotation Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12154–12163. 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 ↗ |
| 별칭 | SS-GAN, Self-supervised GAN, Self-supervised Generative Adversarial Network, GAN with self-supervised auxiliary tasks | SGAN, Semi-GAN, semi-supervised generative adversarial network, GAN-based semi-supervised learning |
| 관련 | 5 | 5 |
| 요약≠ | Self-supervised GAN augments a standard Generative Adversarial Network with one or more self-supervised auxiliary tasks — such as predicting image rotation or patch position — that stabilise adversarial training and yield a discriminator that learns rich, transferable representations from unlabeled data without requiring manual annotations. | 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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