31 citations · 42 across the 4 of their papers we have counts for
4 papers
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
Leon Sixt, Martin Schuessler, Oana-Iuliana Popescu +2
A variety of methods exist to explain image classification models. However, whether they provide any benefit to users over simply comparing various inputs and the model's respectiv…
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments
Martin Schuessler, Philipp Weiß, Leon Sixt
A growing number of approaches exist to generate explanations for image classification. However, few of these approaches are subjected to human-subject evaluations, partly because…
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study
Ahmed Alqaraawi, Martin Schuessler, Philipp Weiß +2
Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various expl…
Minimalistic Explanations: Capturing the Essence of Decisions
Martin Schuessler, Philipp Weiß
The use of complex machine learning models can make systems opaque to users. Machine learning research proposes the use of post-hoc explanations. However, it is unclear if they giv…