Comparar métodos

	GAN débilmente supervisado	Clasificación de imágenes con supervisión débil
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2014–2017	2014–2016
Autor original	Odena et al.; building on Goodfellow et al. (2014)	Multiple contributors; class activation map approach: Zhou et al.
Tipo	Generative model with weak supervision	Weakly supervised deep learning paradigm
Fuente seminal	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.	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.
Alias	WS-GAN, weakly supervised generative adversarial network, label-efficient GAN, semi-labeled GAN	WSL image classification, image-level supervised classification, noisy-label image classification, weakly labeled visual recognition
Relacionados	5	5
Resumen	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.	Weakly supervised image classification trains convolutional or transformer-based networks using only coarse, incomplete, or noisy supervision — such as image-level category labels, hashtags, or web-scraped tags — without requiring precise bounding boxes or pixel annotations. This dramatically reduces labeling cost while still enabling high-accuracy visual recognition at scale.