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20232025
most citedGenerative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes

3 citations · 7 across the 6 of their papers we have counts for

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7 papers

cs.CV2025

Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models

Sangwon Jang, June Suk Choi, Jaehyeong Jo +2

Text-to-image diffusion models have achieved remarkable success in generating high-quality contents from text prompts. However, their reliance on publicly available data and the gr…

cs.LG2025

Continuous Diffusion Model for Language Modeling

Jaehyeong Jo, Sung Ju Hwang

Diffusion models have emerged as a promising alternative to autoregressive models in modeling discrete categorical data. However, diffusion models that directly work on discrete da…

cs.CV2024★ 1 cited

Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models

Sangwon Jang, Jaehyeong Jo, Kimin Lee +1

Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generatin…

cs.LG2023★ 3 cited

Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes

Jaehyeong Jo, Sung Ju Hwang

Learning the distribution of data on Riemannian manifolds is crucial for modeling data from non-Euclidean space, which is required by many applications in diverse scientific fields…

cs.LG2023

Self-Supervised Dataset Distillation for Transfer Learning

Dong Bok Lee, Seanie Lee, Joonho Ko +3

Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples. However, they are not designed to produce a…

cs.LG2023

Drug Discovery with Dynamic Goal-aware Fragments

Seul Lee, Seanie Lee, Kenji Kawaguchi +1

Fragment-based drug discovery is an effective strategy for discovering drug candidates in the vast chemical space, and has been widely employed in molecular generative models. Howe…

cs.LG2023★ 3 cited

DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models

Sohyun An, Hayeon Lee, Jaehyeong Jo +2

Existing NAS methods suffer from either an excessive amount of time for repetitive sampling and training of many task-irrelevant architectures. To tackle such limitations of existi…