321 citations · 347 across the 8 of their papers we have counts for
8 papers
Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance
Yuliia Kaidashova, Bettina Finzel, Ute Schmid
Understanding why a classification model prefers one class over another for an input instance is the challenge of contrastive explanation. This work implements concept-based contra…
When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX
Bettina Finzel, Patrick Hilme, Johannes Rabold +1
Explanations for Convolutional Neural Networks (CNNs) based on relevance of input pixels might be too unspecific to evaluate which and how input features impact model decisions. Es…
Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust
Bettina Finzel, Simon P. Kuhn, David E. Tafler +1
Explaining concepts by contrasting examples is an efficient and convenient way of giving insights into the reasons behind a classification decision. This is of particular interest…
CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition
Ines Rieger, Jaspar Pahl, Bettina Finzel +1
Neural networks are widely adopted, yet the integration of domain knowledge is still underutilized. We propose to integrate domain knowledge about co-occurring facial movements as…
Explanation as a process: user-centric construction of multi-level and multi-modal explanations
Bettina Finzel, David E. Tafler, Stephan Scheele +1
In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge…
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
Gesina Schwalbe, Bettina Finzel
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intellige…
Uncovering the Bias in Facial Expressions
Jessica Deuschel, Bettina Finzel, Ines Rieger
Over the past decades the machine and deep learning community has celebrated great achievements in challenging tasks such as image classification. The deep architecture of artifici…
Verifying Deep Learning-based Decisions for Facial Expression Recognition
Ines Rieger, Rene Kollmann, Bettina Finzel +2
Neural networks with high performance can still be biased towards non-relevant features. However, reliability and robustness is especially important for high-risk fields such as cl…