32 citations · 53 across the 6 of their papers we have counts for
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stat.ML2021
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser +1
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution. We provide an in-depth mathematical analysis of differenc…
stat.ML2019★ 32 cited
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann, Sebastian Lunz, Peter Maass +1
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps…