1 citations · 1 across the 14 of their papers we have counts for
13 papers
Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
Hoseong Hwang, Woorim Han, Joungin Chun +2
To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a single forward pass. However,…
UOTIP: Unbalanced Optimal Transport Map for Unpaired Inverse Problems
Donggyu Lee, Taekyung Lee, Jaewoong Choi
We investigate unpaired image inverse problems, a challenging setting where only independent, non-paired sets of noisy measurements and clean target signals are available for train…
Efficient Adjoint Matching for Fine-tuning Diffusion Models
Jeongwoo Shin, Dongsoo Shin, Yuchen Zhu +5
Reward fine-tuning has become a common approach for aligning pretrained diffusion and flow models with human preferences in text-to-image generation. Among reward-gradient-based me…
Unlearning for One-Step Generative Models via Unbalanced Optimal Transport
Hyundo Choi, Junhyeong An, Jinseong Park +1
Recent advances in one-step generative frameworks, such as flow map models, have significantly improved the efficiency of image generation by learning direct noise-to-data mappings…
Neural Optimal Transport in Hilbert Spaces: Characterizing Spurious Solutions and Gaussian Smoothing
Jae-Hwan Choi, Jiwoo Yoon, Dohyun Kwon +1
We study Neural Optimal Transport in infinite-dimensional Hilbert spaces. In non-regular settings, Semi-dual Neural OT often generates spurious solutions that fail to accurately ca…
Rate-Optimal Noise Annealing in Semi-Dual Neural Optimal Transport: Tangential Identifiability, Off-Manifold Ambiguity, and Guaranteed Recovery
Raymond Chu, Jaewoong Choi, Dohyun Kwon
Semi-dual neural optimal transport learns a transport map via a max-min objective, yet training can converge to incorrect or degenerate maps. We fully characterize these spurious s…
Reweighted Flow Matching via Unbalanced OT for Label-free Long-tailed Generation
Hyunsoo Song, Minjung Gim, Jaewoong Choi
Flow matching has recently emerged as a powerful framework for continuous-time generative modeling. However, when applied to long-tailed distributions, standard flow matching suffe…
APT: Adaptive Personalized Training for Diffusion Models with Limited Data
JungWoo Chae, Jiyoon Kim, JaeWoong Choi +2
Personalizing diffusion models using limited data presents significant challenges, including overfitting, loss of prior knowledge, and degradation of text alignment. Overfitting le…
Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan
Jaemoo Choi, Jaewoong Choi, Dohyun Kwon
We address the convergence problem in learning the Optimal Transport (OT) map, where the OT Map refers to a map from one distribution to another while minimizing the transport cost…
Novelty-focused R&D landscaping using transformer and local outlier factor
Jaewoong Choi
While numerous studies have explored the field of research and development (R&D) landscaping, the preponderance of these investigations has emphasized predictive analysis based on…
Scalable Simulation-free Entropic Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi
The Optimal Transport (OT) problem investigates a transport map that connects two distributions while minimizing a given cost function. Finding such a transport map has diverse app…
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
Milena Gazdieva, Jaemoo Choi, Alexander Kolesov +3
Aggregating data from multiple sources can be formalized as an Optimal Transport (OT) barycenter problem, which seeks to compute the average of probability distributions with respe…
Unsupervised Point Cloud Completion through Unbalanced Optimal Transport
Taekyung Lee, Jaemoo Choi, Jaewoong Choi +1
Unpaired point cloud completion is crucial for real-world applications, where ground-truth data for complete point clouds are often unavailable. By learning a completion map from u…