From Pixels to Perception: Interpretable Predictions via Instance-Wise Grouped Feature Selection
Abstract
Understanding the decision-making process of machine learning models provides valuable insights into the task, the data, and the reasons behind a model’s failures. In this work, we propose a method that performs inherently interpretable predictions through the instance-wise sparsification of input images. To align the sparsification with human perception, we learn the masking in the space of semantically meaningful pixel regions rather than on pixel-level. Additionally, we introduce an explicit way to dynamically determine the required level of sparsity for each instance. We show empirically on semi-synthetic and natural image datasets that our inherently interpretable classifier produces more meaningful, human-understandable predictions than state-of-the-art benchmarks.
Cite
Text
Vandenhirtz and Vogt. "From Pixels to Perception: Interpretable Predictions via Instance-Wise Grouped Feature Selection." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Vandenhirtz and Vogt. "From Pixels to Perception: Interpretable Predictions via Instance-Wise Grouped Feature Selection." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/vandenhirtz2025icml-pixels/)BibTeX
@inproceedings{vandenhirtz2025icml-pixels,
title = {{From Pixels to Perception: Interpretable Predictions via Instance-Wise Grouped Feature Selection}},
author = {Vandenhirtz, Moritz and Vogt, Julia E},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {60833-60856},
volume = {267},
url = {https://mlanthology.org/icml/2025/vandenhirtz2025icml-pixels/}
}