54 citations · 56 across the 9 of their papers we have counts for
10 papers
On the Faithfulness of Post-Hoc Concept Bottleneck Models
Laines Schmalwasser, Jan Blunk, Niklas Penzel +2
Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color. To bridge the gap between opaque deep learning representation…
Analyzing and Fine-Tuning Whisper Models for Multilingual Pilot Speech Transcription in the Cockpit
Kartheek Kumar Reddy Nareddy, Sarah Ternus, Julia Niebling
The developments in transformer encoder-decoder architectures have led to significant breakthroughs in machine translation, Automatic Speech Recognition (ASR), and instruction-base…
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
Laines Schmalwasser, Niklas Penzel, Joachim Denzler +1
Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations le…
Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Detecting and classifying abnormal system states is critical for condition monitoring, but supervised methods often fall short due to the rarity of anomalies and the lack of labele…
Exploiting Text-Image Latent Spaces for the Description of Visual Concepts
Laines Schmalwasser, Jakob Gawlikowski, Joachim Denzler +1
Concept Activation Vectors (CAVs) offer insights into neural network decision-making by linking human friendly concepts to the model's internal feature extraction process. However,…
Functional Tensor Decompositions for Physics-Informed Neural Networks
Sai Karthikeya Vemuri, Tim Büchner, Julia Niebling +1
Physics-Informed Neural Networks (PINNs) have shown continuous and increasing promise in approximating partial differential equations (PDEs), although they remain constrained by th…
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
Ferdinand Rewicki, Jakob Gawlikowski, Julia Niebling +1
The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis…
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images
Sireesha Chamarthi, Katharina Fogelberg, Roman C. Maron +2
The potential of deep neural networks in skin lesion classification has already been demonstrated to be on-par if not superior to the dermatologists diagnosis. However, the perform…
Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
Katharina Fogelberg, Sireesha Chamarthi, Roman C. Maron +2
The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks…
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods ma…