15 citations · 16 across the 2 of their papers we have counts for
4 papers
Diffusion Visual Counterfactual Explanations
Maximilian Augustin, Valentyn Boreiko, Francesco Croce +1
Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are 'small' but 'realistic' semantic changes of the image c…
Out-distribution aware Self-training in an Open World Setting
Maximilian Augustin, Matthias Hein
Deep Learning heavily depends on large labeled datasets which limits further improvements. While unlabeled data is available in large amounts, in particular in image recognition, i…
Adversarial Robustness on In- and Out-Distribution Improves Explainability
Maximilian Augustin, Alexander Meinke, Matthias Hein
Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution…
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse +4
With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query i…