Võrdle meetodeid

	CycleGAN: piltide tõlkimine paarideta andmetega tsüklilise konsistentsi abil	Difusioonimudel	Generatiivne võistlev võrk
Valdkond	Süvaõpe	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2017	2020	2014
Looja	Jun-Yan Zhu et al.	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.
Tüüp	Unsupervised image-to-image translation	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)
Algallikas	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.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Rööpnimetused	Cycle-Consistent Adversarial Networks, Unpaired Image-to-Image Translation, Cycle-GAN, Çevrimsel Tutarlı GAN	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Seotud	3	4	4
Kokkuvõte	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 diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.	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.