31 citations · 68 across the 5 of their papers we have counts for
5 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…
Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision
Milagros Miceli, Martin Schuessler, Tianling Yang
The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annota…
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…