5 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…
Beyond Subtokens: A Rich Character Embedding for Low-resource and Morphologically Complex Languages
Felix Schneider, Maria Gogolev, Sven Sickert +1
Tokenization and sub-tokenization based models like word2vec, BERT and the GPTs are the state-of-the-art in natural language processing. Typically, these approaches have limitation…
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,…