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MS-COCO

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RESEARCH · 1 SOURCE · arXiv cs.AI

Controllable Diverse Sampling via pullback directions for conditional diffusion models

New arXiv preprint (arXiv:2610.14294v1) proposes a training-free technique called 'pullback directions' that uses the local pullback geometry from conditioning-embedding space to image space to identify directions of maximal output sensitivity; by steering sampling trajectories along those directions the method aims to yield more diverse prompt-conditioned image generations and inpaintings without extra optimization. Experiments on MS-COCO and BrushNet report improved diversity while preserving prompt alignment.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv:2610.12781v1 — "Distilled Optimal Transport" for representation alignment from a few paired anchors

The new arXiv preprint (2610.12781v1) proposes 'Distilled Optimal Transport', a method that represents embeddings by their similarities to a small set (N≈100–500) of paired anchors, aligns the two modalities by solving a single entropic optimal transport problem with marginal constraints to reduce hubness, and distills the transport plan into lightweight encoders so the mapping is inductive. The paper reports improved image–text and molecule–text alignment versus prior methods at the same anchor budget on Flickr30k, MS-COCO and ChEBI-20.

5.0