方法对比
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| Semi-supervised GAN× | 自监督生成对抗网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016 | 2019 |
| 提出者≠ | Odena, A.; Salimans, T. et al. | Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. |
| 类型≠ | Semi-supervised generative model | Generative model with self-supervised auxiliary tasks |
| 开创性文献≠ | 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 ↗ | 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 ↗ |
| 别名 | SGAN, Semi-GAN, semi-supervised generative adversarial network, GAN-based semi-supervised learning | SS-GAN, Self-supervised GAN, Self-supervised Generative Adversarial Network, GAN with self-supervised auxiliary tasks |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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