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cs.LG2026
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…
cs.LG2023
On convex decision regions in deep network representations
Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt +5
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{ä}rdenfors' conceptual spaces is a…