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.

本文提出了一种优化自由的快速反事实解释算法，利用多领域图像到图像转换算法，通过训练条件变换器实现转换查询图像到任意反事实类别，并仅需在前向时间内仅使用它就能生成解释，该算法较之其他算法更快且更实际可应用。