15 citations · 18 across the 5 of their papers we have counts for
5 papers · 1 filter
ClusterMine: Robust Label-Free Visual Out-Of-Distribution Detection via Concept Mining from Text Corpora
Nikolas Adaloglou, Diana Petrusheva, Mohamed Asker +2
Large-scale visual out-of-distribution (OOD) detection has witnessed remarkable progress by leveraging vision-language models such as CLIP. However, a significant limitation of cur…
Scaling Up Deep Clustering Methods Beyond ImageNet-1K
Nikolas Adaloglou, Felix Michels, Kaspar Senft +2
Deep image clustering methods are typically evaluated on small-scale balanced classification datasets while feature-based -means has been applied on proprietary billion-scale da…
Rethinking cluster-conditioned diffusion models for label-free image synthesis
Nikolas Adaloglou, Tim Kaiser, Felix Michels +1
Diffusion-based image generation models can enhance image quality when conditioned on ground truth labels. Here, we conduct a comprehensive experimental study on image-level condit…
Exploring the Limits of Deep Image Clustering using Pretrained Models
Nikolas Adaloglou, Felix Michels, Hamza Kalisch +1
We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. Our approach involves self-distillation training of clus…
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
Nikolas Adaloglou, Felix Michels, Tim Kaiser +1
We present a comprehensive experimental study on pretrained feature extractors for visual out-of-distribution (OOD) detection, focusing on adapting contrastive language-image pretr…