Optimal Transport (OT) theory is utilized in generative modeling, with recent advancements focusing on OT-based generative models. These models face challenges like unstable training processes and limited expressivity. UOTM introduces a semi-dual form of Unbalanced Optimal Transport (UOT) problem, showing promising outcomes but lacking τ-robustness. A novel method, UOTM with Scheduled Divergence (UOTM-SD), gradually adjusts the divergence term to improve performance while addressing τ-sensitivity. The equi-Lipschitz continuity of UOTM potential contributes to stable convergence during training.
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by Jaemoo Choi,... às arxiv.org 03-08-2024
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