Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Wasserstein GAN (WGAN)× | Modelo de difusión× | Red Generativa Antagónica× | |
|---|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning | Machine learning |
| Año de origen≠ | 2017 | 2020 | 2014 |
| Autor original≠ | Martín Arjovsky, Soumith Chintala & Léon Bottou | Ho, J., Jain, A. & Abbeel, P. | Goodfellow, I. et al. |
| Tipo≠ | Generative adversarial network variant | Generative deep learning (denoising diffusion) | Generative deep learning (adversarial two-network game) |
| Fuente seminal≠ | Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. International Conference on Machine Learning (ICML), 214–223. link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Alias≠ | WGAN, Earth-Mover GAN, Wasserstein Generative Adversarial Network, Wasserstein-GAN | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Relacionados≠ | 3 | 4 | 4 |
| Resumen≠ | Wasserstein GAN (WGAN) is a generative adversarial network variant introduced by Arjovsky, Chintala, and Bottou in 2017 that replaces the Jensen-Shannon divergence used in the original GAN with the Wasserstein-1 (Earth Mover) distance. This substitution provides a theoretically grounded training objective that yields more stable optimization and a loss value that correlates meaningfully with generated sample quality, addressing the notorious mode collapse and vanishing gradient problems of standard GANs. | 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. |
| ScholarGateConjunto de datos ↗ |
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