6 papers
PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images
Simon Damm, Jonas Ricker, Henning Petzka +1
Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large language models, they allow for e…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
Simon Damm, Mike Laszkiewicz, Johannes Lederer +1
Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient…
Generative Models with ELBOs Converging to Entropy Sums
Jan Warnken, Dmytro Velychko, Simon Damm +2
The evidence lower bound (ELBO) is one of the most central objectives for probabilistic unsupervised learning. For the ELBOs of several generative models and model classes, we here…
Benchmarking the Fairness of Image Upsampling Methods
Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt +2
Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos. While the practical applications of these models…
Learning Sparse Codes with Entropy-Based ELBOs
Dmytro Velychko, Simon Damm, Asja Fischer +1
Standard probabilistic sparse coding assumes a Laplace prior, a linear mapping from latents to observables, and Gaussian observable distributions. We here derive a solely entropy-b…