  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.
