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Jong Chul Ye

13 papers hereh-index 7154 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author7

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.LG4
  • physics.optics1
same name
  • Jong Chul Ye — 19 papers, h 11
  • Jong Chul Ye — 16 papers, h 6
  • Jong Chul Ye — 14 papers, h 4
  • Jong Chul Ye — 6 papers, h 1
  • Jong Chul Ye — 6 papers, h 5
  • Jong Chul Ye — 5 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedGeneralizable Holographic Reconstruction via Amplitude-Only Diffusion Priors

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation

Jeongsol Kim, Beomsu Kim, Jong Chul Ye

Diffusion models (DMs), which enable both image generation from noise and inversion from data, have inspired powerful unpaired image-to-image (I2I) translation algorithms. However,…

cs.CV2024

Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation

Junhyeok Lee, Yujin Oh, Dahyoun Lee +9

Acute ischemic stroke (AIS) requires time-critical decision-making, where inaccurate interpretation of neuroimaging findings can lead to irreversible disability. Diffusion-weighted…

cs.LG2024

Simple ReFlow: Improved Techniques for Fast Flow Models

Beomsu Kim, Yu-Guan Hsieh, Michal Klein +4

Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps, this slows inference and limits applicability to time-critical…

cs.LG2024

(Deep) Generative Geodesics

Beomsu Kim, Michael Puthawala, Jong Chul Ye +1

In this work, we propose to study the global geometrical properties of generative models. We introduce a new Riemannian metric to assess the similarity between any two data points.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.