4 citations · 11 across the 13 of their papers we have counts for
3 papers · 1 filter
On the modality gap and the contrastive loss in multi-modal representation learning
Fabian Mager, Hiba Nassar, Lars Kai Hansen
We study the modality gap in CLIP-style dual-encoder contrastive learning, where image and text embeddings remain misaligned despite being trained in a shared space. We argue that…
Improving Relative Representations with Learned Anchors and Whitened Inner Products
Oscar Thorsted Svendsen, Nikolaj Holst Jakobsen, Fabian Mager +1
Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI systems. While Relative Represent…
On What We Can Learn from Low-Resolution Data
Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar +1
Artificial intelligence systems typically rely on large, centrally collected datasets, a premise that does not hold in many real-world domains such as healthcare and public institu…