  We present CartoonX (Cartoon Explanation), a novel model-agnostic explanation
method tailored towards image classifiers and based on the rate-distortion
explanation (RDE) framework. Natural images are roughly piece-wise smooth
signals -- also called cartoon-like images -- and tend to be sparse in the
wavelet domain. CartoonX is the first explanation method to exploit this by
requiring its explanations to be sparse in the wavelet domain, thus extracting
the relevant piece-wise smooth part of an image instead of relevant
pixel-sparse regions. We demonstrate that CartoonX can reveal novel valuable
explanatory information, particularly for misclassifications. Moreover, we show
that CartoonX achieves a lower distortion with fewer coefficients than other
state-of-the-art methods.

Artificial Intelligence;Computer Vision and Pattern Recognition