8 citations · 17 across the 11 of their papers we have counts for
4 papers · 1 filter
Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions
Sujan Sai Gannamaneni, Rohil Prakash Rao, Michael Mock +2
Slice discovery methods (SDMs) are prominent algorithms for finding systematic weaknesses in DNNs. They identify top-k semantically coherent slices/subsets of data where a DNN-unde…
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka +8
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since si…
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities
Julia Rosenzweig, Joachim Sicking, Sebastian Houben +2
An important pillar for safe machine learning (ML) is the systematic mitigation of weaknesses in neural networks to afford their deployment in critical applications. An ubiquitous…
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation
Linara Adilova, Elena Schulz, Maram Akila +4
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge. However, learnin…