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| Generative Adversarial Network× | Diffusion Model× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2014 | 2020 |
| Urheber≠ | Goodfellow, I. et al. | Ho, J., Jain, A. & Abbeel, P. |
| Typ≠ | Generative deep learning (adversarial two-network game) | Generative deep learning (denoising diffusion) |
| Wegweisende Quelle≠ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Aliasnamen≠ | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | 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. |
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