Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Пояснювані GAN (Explainable GAN)× | Дифузійна модель× | |
|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2019 (GAN Dissection); ongoing | 2020 |
| Автор методу≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Ho, J., Jain, A. & Abbeel, P. |
| Тип≠ | Explainable generative model | Generative deep learning (denoising diffusion) |
| Основоположне джерело≠ | 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 ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Інші назви≠ | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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 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. |
| ScholarGateНабір даних ↗ |
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