ICML 2025

From Pixels to Perception:
Interpretable Predictions via Instance-wise Grouped Feature Selection
Moritz Vandenhirtz 1 Julia E. Vogt 1
Abstract
Understanding the decision-making process of
machine learning models provides valuable in-
sights into the task, the data, and the reasons be-
hind a model’s failures. In this work, we propose
a method that performs inherently interpretable
predictions through the instance-wise sparsifica-
tion of input images. To align the sparsification
with human perception, we learn the masking in
the space of semantically meaningful pixel re-
gions rather than on pixel-level. Additionally, we
introduce an explicit way to dynamically deter-
mine the required level of sparsity for each in-
stance. We show empirically on semi-synthetic
and natural image datasets that our inherently in-
terpretable classifier produces more meaningful,
human-understandable predictions than state-of-
the-art benchmarks.
1. Introduction
Knowledge is power (Bacon, 1597). Knowing how ma-
chine learning models make their predictions empowers
users to understand the underlying patterns in the data, iden-
tify potential biases, reason about their safety, and build
trust in the models’ decisions. In high-stakes domains,
where model decisions can have significant consequences,
the field of interpretability is crucial to provide this under-
standing (Doshi-Velez & Kim, 2017). In this work, we focus
on providing inherent interpretability – a subfield where the
explanations are necessarily faithful to what the model com-
putes (Rudin, 2019) – through the lens of sparsity in the
input features (Lipton, 2016; Marcinkeviˇcs & Vogt, 2023).
This constraint makes it easier to understand which specific
inputs contribute to an output (Miller, 1956), leading to
more human-understandable predictions.
1Department
of
Computer
Science,
ETH
Zurich,
Switzerland.
Correspondence
to:
Moritz
Vandenhirtz
<moritz.vandenhirtz@inf.ethz.ch>.
Proceedings of the 42 nd International Conference on ...