方法对比
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| 可解释生成对抗网络× | 可解释图像分类× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 (GAN Dissection); ongoing | 2016-2017 |
| 提出者≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME) |
| 类型≠ | Explainable generative model | Post-hoc explainability applied to image classifiers |
| 开创性文献≠ | 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 ↗ | Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618-626. DOI ↗ |
| 别名 | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition |
| 相关 | 4 | 4 |
| 摘要≠ | 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. | Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is to make the classifier's decision process transparent, auditable, and trustworthy. |
| ScholarGate数据集 ↗ |
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