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

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

Exploring the Limits of Deep Image Clustering using Pretrained Models. - Nikolas Adaloglou; Felix Michels; Hamza Kalisch; Markus Kollmann (BMVC, 2023)

Contrastive Language-Image Pretrained (CLIP) Models are Powerful Out-of-Distribution Detectors. - Felix Michels; Nikolas Adaloglou; Tim Kaiser; Markus Kollmann (CoRR, 2023)

Rethinking cluster-conditioned diffusion models. - Nikolas Adaloglou; Tim Kaiser; Felix Michels; Markus Kollmann (CoRR, 2024)

Scaling Up Deep Clustering Methods Beyond ImageNet-1K. - Nikolas Adaloglou; Felix Michels; Kaspar Senft; Diana Petrusheva; Markus Kollmann (CoRR, 2024)

Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection. - Nikolas Adaloglou; Felix Michels; Tim Kaiser; Markus Kollmann (Trans. Mach. Learn. Res., 2024)

Rethinking Cluster-Conditioned Diffusion Models for Label-Free Image Synthesis. - Nikolas Adaloglou; Tim Kaiser; Felix Michels; Markus Kollmann (WACV, 2025)