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cs.CV2025
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…
cs.CV2022★ 2 cited
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings
Gesina Schwalbe, Christian Wirth, Ute Schmid
One major drawback of deep convolutional neural networks (CNNs) for use in safety critical applications is their black-box nature. This makes it hard to verify or monitor complex,…