Compară metode

	CycleGAN: Traducere de imagini fără perechi cu consistență ciclică	Rețea Generativă Adversarial
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2017	2014
Autorul original	Jun-Yan Zhu et al.	Goodfellow, I. et al.
Tip	Unsupervised image-to-image translation	Generative deep learning (adversarial two-network game)
Sursa seminală	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.
Denumiri alternative	Cycle-Consistent Adversarial Networks, Unpaired Image-to-Image Translation, Cycle-GAN, Çevrimsel Tutarlı GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Înrudite	3	4
Rezumat	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.