12 citations · 12 across the 2 of their papers we have counts for
3 papers
How a Model's Architecture and Performance Influences Explainability: A Study of Grad-CAM and CRAFT in CNN-Based Image Classification
Kilian Göller, Haadia Amjad, Steffen Seitz +2
Deep learning models, particularly convolutional neural networks (CNNs), are widely adopted in computer vision, yet their opaque decision-making raises concerns in high-risk applic…
Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation
Haadia Amjad, Kilian Goeller, Steffen Seitz +4
Deep learning is actively being used in biometrics to develop efficient identification and verification systems. Handwritten signatures are a common subset of biometric data for au…
Interpretability is in the eye of the beholder: Human versus artificial classification of image segments generated by humans versus XAI
Romy Müller, Marius Thoß, Julian Ullrich +2
The evaluation of explainable artificial intelligence is challenging, because automated and human-centred metrics of explanation quality may diverge. To clarify their relationship,…