Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Generatiivinen kilpaileva verkko× | Diffuusiomalli× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2014 | 2020 |
| Kehittäjä≠ | Goodfellow, I. et al. | Ho, J., Jain, A. & Abbeel, P. |
| Tyyppi≠ | Generative deep learning (adversarial two-network game) | Generative deep learning (denoising diffusion) |
| Alkuperäislähde≠ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Rinnakkaisnimet≠ | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
|
|