Maximilian Augustin

According to our database, Maximilian Augustin authored at least 11 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences, Neuron Visualisations, and Visual Counterfactual Explanations.

Maximilian Augustin, Yannic Neuhaus, Matthias Hein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Analyzing and Explaining Image Classifiers via Diffusion Guidance.

Maximilian Augustin, Yannic Neuhaus, Matthias Hein

CoRR, 2023

Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet.

Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko, Matthias Hein

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Diffusion Visual Counterfactual Explanations.

Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities.

Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein

Proceedings of the International Conference on Machine Learning, 2022

Sparse Visual Counterfactual Explanations in Image Space.

Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein

Proceedings of the Pattern Recognition, 2022

2021

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service.

Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Maximilian Augustin, Michael Backes, Mario Fritz

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Out-distribution aware Self-training in an Open World Setting.

Maximilian Augustin, Matthias Hein

CoRR, 2020

Adversarial Robustness on In- and Out-Distribution Improves Explainability.

Maximilian Augustin, Alexander Meinke, Matthias Hein

Proceedings of the Computer Vision - ECCV 2020, 2020

2018

Towards Reverse-Engineering Black-Box Neural Networks.

Seong Joon Oh, Max Augustin, Mario Fritz, Bernt Schiele

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Whitening Black-Box Neural Networks.

Seong Joon Oh, Max Augustin, Bernt Schiele, Mario Fritz

CoRR, 2017