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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…
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…
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…
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…