Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-19775-8_26 | European Conference on Computer Vision |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Kolek | 1 | 0 | 0.68 |
| Anh Nguyen-Duc | 2 | 73 | 23.27 |
| Ron Levie | 3 | 0 | 0.68 |
| Joan Bruna | 4 | 0 | 0.34 |
| Gitta Kutyniok | 5 | 325 | 34.77 |