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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…
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…
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…