Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Réseau antagoniste génératif× | Modèle de diffusion× | |
|---|---|---|
| Domaine | Apprentissage profond | Apprentissage profond |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2014 | 2020 |
| Auteur d'origine≠ | Goodfellow, I. et al. | Ho, J., Jain, A. & Abbeel, P. |
| Type≠ | Generative deep learning (adversarial two-network game) | Generative deep learning (denoising diffusion) |
| Source fondatrice≠ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Alias≠ | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Apparentées | 4 | 4 |
| Résumé≠ | 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. | 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. |
| ScholarGateJeu de données ↗ |
|
|