विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| व्याख्या योग्य प्रसार मॉडल× | व्याख्यायोग्य GAN (Explainable GAN)× | |
|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2020–2022 | 2019 (GAN Dissection); ongoing |
| प्रवर्तक≠ | Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers | Bau, D. et al. (GAN Dissection); broader XAI-GAN community |
| प्रकार≠ | Generative model with post-hoc or intrinsic explainability | Explainable generative model |
| मौलिक स्रोत≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗ | 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 ↗ |
| उपनाम | XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model |
| संबंधित≠ | 6 | 4 |
| सारांश≠ | An Explainable Diffusion Model couples a denoising diffusion probabilistic model with post-hoc or intrinsic explainability techniques — such as SHAP, gradient-based saliency, attention analysis, or concept-based probing — so that each generative or predictive decision can be audited and justified rather than treated as a black box. | 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. |
| ScholarGateडेटासेट ↗ |
|
|