Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle de diffusion× | Machine à vecteurs de support (Classification)× | |
|---|---|---|
| Domaine≠ | Apprentissage profond | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2020 | 1995 |
| Auteur d'origine≠ | Ho, J., Jain, A. & Abbeel, P. | Cortes, C. & Vapnik, V. |
| Type≠ | Generative deep learning (denoising diffusion) | Maximum-margin classifier (kernel method) |
| Source fondatrice≠ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Alias≠ | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
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