手法を比較

	自己教師ありGAN (Self-supervised GAN)	Generative Adversarial Network
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2019	2014
提唱者	Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N.	Goodfellow, I. et al.
種類	Generative model with self-supervised auxiliary tasks	Generative deep learning (adversarial two-network game)
原典	Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019). Self-Supervised GANs via Auxiliary Rotation Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12154–12163.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
別名	SS-GAN, Self-supervised GAN, Self-supervised Generative Adversarial Network, GAN with self-supervised auxiliary tasks	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
関連	5	4
概要	Self-supervised GAN augments a standard Generative Adversarial Network with one or more self-supervised auxiliary tasks — such as predicting image rotation or patch position — that stabilise adversarial training and yield a discriminator that learns rich, transferable representations from unlabeled data without requiring manual annotations.	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.