164 citations · 468 across the 23 of their papers we have counts for
43 papers
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam, Ahcene Boubekki, Stine Hansen +4
The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the…
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen +1
The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing…
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Stine Hansen, Srishti Gautam, Robert Jenssen +1
Recent work has shown that label-efficient few-shot learning through self-supervision can achieve promising medical image segmentation results. However, few-shot segmentation model…
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
Srishti Gautam, Marina M. -C. Höhne, Stine Hansen +2
The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to impr…
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Srishti Gautam, Marina M. -C. Höhne, Stine Hansen +2
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the unde…