3 papers
cs.LG2026
Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Models
Sidney Bender, Marco Morik
Foundation models, despite their robust zero-shot capabilities, remain vulnerable to spurious correlations and 'Clever Hans' strategies. Existing mitigation methods often rely on u…
cs.CV2024
Objective drives the consistency of representational similarity across datasets
Laure Ciernik, Lorenz Linhardt, Marco Morik +3
The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irre…
cs.LG2024
An Analysis of Human Alignment of Latent Diffusion Models
Lorenz Linhardt, Marco Morik, Sidney Bender +1
Diffusion models, trained on large amounts of data, showed remarkable performance for image synthesis. They have high error consistency with humans and low texture bias when used f…