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| Semi-supervised GAN× | GAN Kendiri-terawasi× | |
|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2016 | 2019 |
| Pengasas≠ | Odena, A.; Salimans, T. et al. | Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. |
| Jenis≠ | Semi-supervised generative model | Generative model with self-supervised auxiliary tasks |
| Sumber perintis≠ | 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 ↗ |
| Alias | 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 |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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