विधियों की तुलना करें

	कमज़ोर पर्यवेक्षित GAN (Weakly Supervised GAN)	अर्ध-पर्यवेक्षित GAN
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2014–2017	2016
प्रवर्तक	Odena et al.; building on Goodfellow et al. (2014)	Odena, A.; Salimans, T. et al.
प्रकार	Generative model with weak supervision	Semi-supervised generative model
मौलिक स्रोत	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.
उपनाम	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
संबंधित	5	5
सारांश	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.