论文标题
快速实时反事实解释
Fast Real-time Counterfactual Explanations
论文作者
论文摘要
考虑了反事实解释，这是为了回答{\ us预测是A类而不是B类。由Stargan构建的变压器是在限制的分类器上训练的残留生成器，该分类器受到扰动损失的约束，该分类器维持查询图像的内容信息，但仅更改了特定于类的语义信息。变压器可以将查询图像传输到任何反事实类，在推断期间，我们的解释只能在向前的时间内生成。它很快并且可以满足实时应用。由于对GAN的对抗性培训，与其他同行相比，我们的解释也更现实。实验结果表明，就质量和速度而言，我们的建议比现有的最新状态更好。
Counterfactual explanations are considered, which is to answer {\it why the prediction is class A but not B.} Different from previous optimization based methods, an optimization-free Fast ReAl-time Counterfactual Explanation (FRACE) algorithm is proposed benefiting from the development of multi-domain image to image translation algorithms. Built from starGAN, a transformer is trained as a residual generator conditional on a classifier constrained under a proposal perturbation loss which maintains the content information of the query image, but just the class-specific semantic information is changed. The transformer can transfer the query image to any counterfactual class, and during inference, our explanation can be generated by it only within a forward time. It is fast and can satisfy the real-time practical application. Because of the adversarial training of GAN, our explanation is also more realistic compared to other counterparts. The experimental results demonstrate that our proposal is better than the existing state of the art in terms of quality and speed.
