Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| GAN Explicable× | Red Generativa Antagónica× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2019 (GAN Dissection); ongoing | 2014 |
| Autor original≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Goodfellow, I. et al. |
| Tipo≠ | Explainable generative model | Generative deep learning (adversarial two-network game) |
| Fuente seminal≠ | Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019). link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Alias | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Relacionados | 4 | 4 |
| Resumen≠ | Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable. | 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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