Сравнение методов

	CycleGAN	Генеративно-состязательная сеть
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2017	2014
Автор метода	Jun-Yan Zhu et al.	Goodfellow, I. et al.
Тип	Unsupervised image-to-image translation	Generative deep learning (adversarial two-network game)
Основополагающий источник	Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE International Conference on Computer Vision (ICCV), 2242–2251.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Другие названия	Cycle-Consistent Adversarial Networks, Unpaired Image-to-Image Translation, Cycle-GAN, Çevrimsel Tutarlı GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Связанные	3	4
Сводка	CycleGAN, introduced by Zhu et al. at ICCV 2017, learns to translate images between two visual domains without requiring paired training examples. It trains two generators and two discriminators simultaneously, enforcing a cycle-consistency constraint so that an image translated from domain X to Y and back again recovers the original. This makes it applicable whenever large aligned datasets are unavailable, such as converting photographs to artwork styles, turning summer landscapes into winter scenes, or mapping satellite imagery to map tiles.	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.