1 code implementation • 24 Jan 2024 • Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos.
no code implementations • 22 Jun 2023 • Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques.
no code implementations • 26 May 2023 • Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer
Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution.
1 code implementation • 21 Jun 2022 • Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Learning the tail behavior of a distribution is a notoriously difficult problem.
1 code implementation • ICML Workshop INNF 2021 • Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations.
1 code implementation • 1 May 2020 • Mike Laszkiewicz, Asja Fischer, Johannes Lederer
Many Machine Learning algorithms are formulated as regularized optimization problems, but their performance hinges on a regularization parameter that needs to be calibrated to each application at hand.