Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Генеративно-состязательная сеть× | Генеративная модель на основе градиента (score-based)× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2014 | 2019 |
| Автор метода≠ | Goodfellow, I. et al. | Song, Y. & Ermon, S. |
| Тип≠ | Generative deep learning (adversarial two-network game) | Score-based generative model (SDE framework) |
| Основополагающий источник≠ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907. link ↗ |
| Другие названия | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Skor Tabanlı Üretici Model (Score-Based / SDE), score-based diffusion, SDE-based generative model, score SDE |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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 score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation. |
| ScholarGateНабор данных ↗ |
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