পদ্ধতির তুলনা করুন

	দুর্বলভাবে তত্ত্বাবধানে থাকা GAN	জেনারেটিভ অ্যাডভারসারিয়াল নেটওয়ার্ক
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2014–2017	2014
প্রবর্তক	Odena et al.; building on Goodfellow et al. (2014)	Goodfellow, I. et al.
ধরন	Generative model with weak supervision	Generative deep learning (adversarial two-network game)
মৌলিক উৎস	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.
অপর নাম	WS-GAN, weakly supervised generative adversarial network, label-efficient GAN, semi-labeled GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
সম্পর্কিত	5	4
সারসংক্ষেপ	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.