Powerful machine learning models such as deep neural networks are inherently opaque, which has motivated numerous explanation methods that the research community developed over the last decade [1, 24, 26, 20, 15, 16, 7, 2]. The meaning and validity of an explanation depends on the underlying principle of the explanation framework. Therefore, a trustworthy explanation framework must align intuition with mathematical rigor while maintaining maximal flexibility and applicability. We believe the Rate-Distortion Explanation (RDE) framework, first proposed by [16], then extended by [9], as well as the similar framework in [2], meets the desired qualities. In this chapter, we aim to present the RDE framework in a revised and holistic manner. Our generalized RDE framework can be applied to any model (not just classification tasks), supports in-distribution interpretability (by leveraging in-painting GANs), and admits interpretation queries (by considering suitable input signal representations).

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 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 \emph{relevant piece-wise smooth} part of an image instead of relevant pixel-sparse regions. We demonstrate experimentally that CartoonX is not only highly interpretable due to its piece-wise smooth nature but also particularly apt at explaining misclassifications.