विधियों की तुलना करें

	कमज़ोर पर्यवेक्षित GAN (Weakly Supervised GAN)	जनरेटिव एडवरसैरियल नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2014–2017	2014
प्रवर्तक	Odena et al.; building on Goodfellow et al. (2014)	Goodfellow, I. et al.
प्रकार	Generative model with weak supervision	Generative deep learning (adversarial two-network game)
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.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
उपनाम	WS-GAN, weakly supervised generative adversarial network, label-efficient GAN, semi-labeled GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
संबंधित	5	4
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.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.