Reproducibility Study of ”CartoonX: Cartoon Explanations of Image Classifiers”

Aditya Prakash Patra, Sina Taslimi, Luke Chin A Foeng, Pratik Kayal

Keywords: rescience c, machine learning, Explainable AI, Image Classification, Wavelets

Abstract: In this reproducibility study, we verify the claims and contributions in Cartoon Explanations of Image Classifiers by Kolek et al.. These include (i) A proposed technique named CartoonX used to extract visual explanations for predictions via image classification networks, (ii) CartoonX being able to reveal piece‐wise smooth regions of the image, unlike previous methods, which extract relevant pixel‐sparse regions, and (iii) CartoonX achieving lower distortion values than these methods.

Paper Url: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136720439.pdf

Paper Venue: ECCV 2022

Confirmation: The report pdf is generated from the provided camera ready Google Colab script, The report metadata is verified from the camera ready Google Colab script, The report contains correct author information., The report contains link to code and SWH metadata., The report follows the ReScience latex style guides as in the Reproducibility Report Template (https://paperswithcode.com/rc2022/registration)., The report contains the Reproducibility Summary in the first page., The latex .zip file is verified from the camera ready Google Colab script

Paper Review Url: https://openreview.net/forum?id=RYTBAtyXqJ

Journal: ReScience Volume 9 Issue 2 Article 29

Doi: https://www.doi.org/10.5281/zenodo.8173721

Code: https://archive.softwareheritage.org/swh:1:dir:699d0b641cd3fd9fad8247d19f2f88648e6d72cd