Automatic localization and decoding of honeybee markers using deep convolutional neural networks. - Benjamin Wild; Leon Sixt; Tim Landgraf (CoRR, 2018)

RenderGAN: Generating Realistic Labeled Data. - Leon Sixt; Benjamin Wild; Tim Landgraf (Frontiers Robotics AI, 2018)

When Explanations Lie: Why Modified BP Attribution Fails. - Leon Sixt; Maximilian Granz; Tim Landgraf (CoRR, 2019)

Restricting the Flow: Information Bottlenecks for Attribution. - Karl Schulz; Leon Sixt; Federico Tombari; Tim Landgraf (ICLR, 2020)

When Explanations Lie: Why Many Modified BP Attributions Fail. - Leon Sixt; Maximilian Granz; Tim Landgraf (ICML, 2020)

Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments. - Martin Schuessler; Philipp Weiß; Leon Sixt (CoRR, 2021)

Analyzing a Caching Model. - Leon Sixt; Evan Zheran Liu; Marie Pellat; James Wexler; Milad Hashemi; Been Kim; Martin Maas 001 (CoRR, 2021)

Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset. - Leon Sixt; Martin Schuessler; Oana-Iuliana Popescu; Philipp Weiß; Tim Landgraf (ICLR, 2022)

DNNR: Differential Nearest Neighbors Regression. - Youssef Nader; Leon Sixt; Tim Landgraf (ICML, 2022)

A Rigorous Study Of The Deep Taylor Decomposition. - Leon Sixt; Tim Landgraf (Trans. Mach. Learn. Res., 2022)

Enhancing And Evaluating Interpretability In Machine Learning Through Theory And Practice. - Leon Sixt (2023)