  Optimal Transport (OT) problem investigates a transport map that bridges two
distributions while minimizing a given cost function. In this regard, OT
between tractable prior distribution and data has been utilized for generative
modeling tasks. However, OT-based methods are susceptible to outliers and face
optimization challenges during training. In this paper, we propose a novel
generative model based on the semi-dual formulation of Unbalanced Optimal
Transport (UOT). Unlike OT, UOT relaxes the hard constraint on distribution
matching. This approach provides better robustness against outliers, stability
during training, and faster convergence. We validate these properties
empirically through experiments. Moreover, we study the theoretical upper-bound
of divergence between distributions in UOT. Our model outperforms existing
OT-based generative models, achieving FID scores of 2.97 on CIFAR-10 and 6.36
on CelebA-HQ-256. The code is available at
\url{https://github.com/Jae-Moo/UOTM}.
