On the Vulnerability of Capsule Networks to Adversarial Attacks. - Felix Michels; Tobias Uelwer; Eric Upschulte; Stefan Harmeling (CoRR, 2019)

Fast Multi-Level Foreground Estimation. - Thomas Germer; Tobias Uelwer; Stefan Conrad 001; Stefan Harmeling (ICPR, 2020)

Phase Retrieval Using Conditional Generative Adversarial Networks. - Tobias Uelwer; Alexander Oberstraß; Stefan Harmeling (ICPR, 2020)

Discrete Latent Space World Models for Reinforcement Learning. - Jan Robine; Tobias Uelwer; Stefan Harmeling (CoRR, 2020)

PyMatting: A Python Library for Alpha Matting. - Thomas Germer; Tobias Uelwer; Stefan Conrad 001; Stefan Harmeling (J. Open Source Softw., 2020)

Non-iterative Phase Retrieval with Cascaded Neural Networks. - Tobias Uelwer; Tobias Hoffmann; Stefan Harmeling (ICANN (2), 2021)

Learning to Plan via a Multi-step Policy Regression Method. - Stefan Wagner 020; Michael Janschek; Tobias Uelwer; Stefan Harmeling (ICANN (4), 2021)

Learning to Detect Adversarial Examples Based on Class Scores. - Tobias Uelwer; Felix Michels; Oliver De Candido (KI, 2021)

A Closer Look at Reference Learning for Fourier Phase Retrieval. - Tobias Uelwer; Nick Rucks; Stefan Harmeling (CoRR, 2021)

Deblurring Photographs of Characters Using Deep Neural Networks. - Thomas Germer; Tobias Uelwer; Stefan Harmeling (CoRR, 2022)

Optimizing Intermediate Representations of Generative Models for Phase Retrieval. - Tobias Uelwer; Sebastian Konietzny; Stefan Harmeling (Trans. Mach. Learn. Res., 2022)

Transformer-based World Models Are Happy With 100k Interactions. - Jan Robine; Marc Höftmann; Tobias Uelwer; Stefan Harmeling (ICLR, 2023)

A Survey on Self-Supervised Representation Learning. - Tobias Uelwer; Jan Robine; Stefan Sylvius Wagner; Marc Höftmann; Eric Upschulte; Sebastian Konietzny; Maike Behrendt; Stefan Harmeling (CoRR, 2023)

Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data. - Thomas Germer; Jan Robine; Sebastian Konietzny; Stefan Harmeling; Tobias Uelwer (CoRR, 2023)

Learning Conditional Generative Models for Phase Retrieval. - Tobias Uelwer; Sebastian Konietzny; Alexander Oberstraß; Stefan Harmeling (J. Mach. Learn. Res., 2023)

Approaching phase retrieval with deep learning - Tobias Uelwer (2023)

Smaller World Models for Reinforcement Learning. - Jan Robine; Tobias Uelwer; Stefan Harmeling (Neural Process. Lett., 2023)

Resource-Efficient Anonymization of Textual Data via Knowledge Distillation from Large Language Models. - Tobias Deußer; Max Hahnbück; Tobias Uelwer; Cong Zhao; Christian Bauckhage; Rafet Sifa (COLING (Industry), 2025)

A survey on self-supervised methods for visual representation learning. - Tobias Uelwer; Jan Robine; Stefan Sylvius Wagner; Marc Höftmann; Eric Upschulte; Sebastian Konietzny; Maike Behrendt; Stefan Harmeling (Mach. Learn., 2025)