قارن الطرق

	شبكات الخصومة التوليدية ذاتية الإشراف	شبكة الخصومة التوليدية شبه الخاضعة للإشراف
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2019	2016
صاحب الطريقة	Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N.	Odena, A.; Salimans, T. et al.
النوع	Generative model with self-supervised auxiliary tasks	Semi-supervised generative model
المصدر التأسيسي	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.	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.
الأسماء البديلة	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
ذات صلة	5	5
الملخص	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.