2 citations · 3 across the 7 of their papers we have counts for
6 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…
Guiding a diffusion model using sliding windows
Nikolas Adaloglou, Tim Kaiser, Damir Iagudin +1
Guidance is a widely used technique for diffusion models to enhance sample quality. Technically, guidance is realised by using an auxiliary model that generalises more broadly than…
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…