论文标题
清晰度：改进的梯度方法，用于产生高质量的视觉反事实解释
Clarity: an improved gradient method for producing quality visual counterfactual explanations
论文作者
论文摘要
视觉反事实说明确定对图像的修改，以改变分类器的预测。我们提出了一组基于生成模型（VAE）和一个直接在潜在空间中训练的分类器集合的技术，它们共同提高了计算视觉反事实所需的梯度质量。这些改进导致了一种新颖的分类模型Clarity，该模型在所有图像上产生了现实的反事实解释。我们还提出了几项实验，可以深入了解这些技术为何与文献相比，这些技术会带来更好的质量结果。所产生的解释与最先进的竞争性，并强调选择有意义的培训空间的重要性。
Visual counterfactual explanations identify modifications to an image that would change the prediction of a classifier. We propose a set of techniques based on generative models (VAE) and a classifier ensemble directly trained in the latent space, which all together, improve the quality of the gradient required to compute visual counterfactuals. These improvements lead to a novel classification model, Clarity, which produces realistic counterfactual explanations over all images. We also present several experiments that give insights on why these techniques lead to better quality results than those in the literature. The explanations produced are competitive with the state-of-the-art and emphasize the importance of selecting a meaningful input space for training.
