Optimal Transport theory enhances stability and performance in generative modeling.
The author integrates various OT-based GANs, emphasizing the importance of strictly convex functions and the cost function in enhancing training stability and preventing mode collapse. Additionally, a novel method is proposed to address the τ-sensitivity of UOTM while improving performance.