4 papers
Suppressing Non-Semantic Noise in Masked Image Modeling Representations
Martine Hjelkrem-Tan, Marius Aasan, Rwiddhi Chakraborty +3
Masked Image Modeling (MIM) has become a ubiquitous self-supervised vision paradigm. In this work, we show that MIM objectives cause the learned representations to retain non-seman…
Why Prototypes Collapse: Diagnosing and Preventing Partial Collapse in Prototypical Self-Supervised Learning
Gabriel Y. Arteaga, Marius Aasan, Rwiddhi Chakraborty +4
Prototypical self-supervised learning methods consistently suffer from partial prototype collapse, where multiple prototypes converge to nearly identical representations. This unde…
SPoT: Subpixel Placement of Tokens in Vision Transformers
Martine Hjelkrem-Tan, Marius Aasan, Gabriel Y. Arteaga +1
Vision Transformers naturally accommodate sparsity, yet standard tokenization methods confine features to discrete patch grids. This constraint prevents models from fully exploitin…
Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski
Uncertainty estimation is a necessary component when implementing AI in high-risk settings, such as autonomous cars, medicine, or insurances. Large Language Models (LLMs) have seen…