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Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Difúzny model× | Generatívna protiadverзárna sieť× | |
|---|---|---|
| Odbor | Hlboké učenie | Hlboké učenie |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2020 | 2014 |
| Tvorca≠ | Ho, J., Jain, A. & Abbeel, P. | Goodfellow, I. et al. |
| Typ≠ | Generative deep learning (denoising diffusion) | Generative deep learning (adversarial two-network game) |
| Pôvodný zdroj≠ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Ďalšie názvy≠ | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Príbuzné | 4 | 4 |
| Zhrnutie≠ | 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. |
| ScholarGateDátová sada ↗ |
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