Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo de difusión semi-supervisado× | Red Generativa Antagónica× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2020–2022 | 2014 |
| Autor original≠ | Multiple groups (Ho et al., Song et al., and successors) | Goodfellow, I. et al. |
| Tipo≠ | Generative model with semi-supervised guidance | Generative deep learning (adversarial two-network game) |
| Fuente seminal≠ | Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2256–2265. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Alias | Semi-supervised DDPM, Label-guided diffusion model, Semi-supervised score-based generative model, SSL diffusion | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Relacionados≠ | 3 | 4 |
| Resumen≠ | A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-conditioned outputs while still exploiting the structure in unlabeled data. | 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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