Salīdzināt metodes

	Puss-uzraudzītā difūzijas modelis	Generatīvais Adversariālais Tīkls
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020–2022	2014
Autors	Multiple groups (Ho et al., Song et al., and successors)	Goodfellow, I. et al.
Tips	Generative model with semi-supervised guidance	Generative deep learning (adversarial two-network game)
Pirmavots	Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2256–2265.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Citi nosaukumi	Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Saistītās	3	4
Kopsavilkums	A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-conditioned outputs while still exploiting the structure in unlabeled data.	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.