So sánh phương pháp

	GAN Giám sát Yếu	GAN bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014–2017	2016
Người khởi xướng	Odena et al.; building on Goodfellow et al. (2014)	Odena, A.; Salimans, T. et al.
Loại	Generative model with weak supervision	Semi-supervised generative model
Công trình gốc	Odena, A., Olah, C., & Shlens, J. (2017). Conditional Image Synthesis with Auxiliary Classifier GANs. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70, 2642–2651.	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.
Tên gọi khác	WS-GAN, weakly supervised generative adversarial network, label-efficient GAN, semi-labeled GAN	SGAN, Semi-GAN, semi-supervised generative adversarial network, GAN-based semi-supervised learning
Liên quan	5	5
Tóm tắt	A Weakly Supervised GAN is a generative adversarial network trained with partially labeled, noisily labeled, or coarse-annotation data instead of fully annotated ground truth. It extends the standard GAN framework so that limited supervision guides conditional generation or discriminative learning, enabling high-quality data synthesis and classification in label-scarce settings.	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.