让视觉模型的决策过程更透明，用最小改动生成可信解释。
Faithful Counterfactual Visual Explanations (FCVE)
- 通过识别模型内部概念生成最小改动的反事实图像
- 解释结果忠实反映模型真实决策逻辑
- 适合希望理解AI判断依据的研究者与使用者
计算机视觉中的深度学习模型取得了显著进展，但其缺乏透明性和可解释性仍是挑战。可解释AI的发展有助于提升模型的理解与性能。然而，现有技术常难以提供让非专家轻易理解的可信解释，且无法准确揭示模型内在决策机制。为此，我们提出一种平衡合理性与忠实性的反事实解释（CE）模型。该模型通过识别模型内部学习的概念和特征，而非修改像素数据，在图像中进行最小必要调整，生成易于理解的视觉解释。所生成的解释能真实反映模型的决策过程，确保对模型的高度忠实性。
原文摘要 · Abstract (English)
Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models' intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-to-understand visual explanations by making minimum changes necessary in images without altering the pixel data. Instead, the proposed method identifies internal concepts and filters learned by models and leverages them to produce plausible counterfactual explanations. The provided explanations reflect the internal decision-making process of the model, thus ensuring faithfulness to the model.
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