مقایسهٔ روشها
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| شبکه مولد متخاصم قابل توضیح× | مدل انتشار (Diffusion Model)× | |
|---|---|---|
| حوزه | یادگیری عمیق | یادگیری عمیق |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 2019 (GAN Dissection); ongoing | 2020 |
| پدیدآور≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Ho, J., Jain, A. & Abbeel, P. |
| نوع≠ | Explainable generative model | Generative deep learning (denoising diffusion) |
| منبع بنیادین≠ | 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 ↗ |
| نامهای دیگر≠ | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| مرتبط | 4 | 4 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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