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Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Generative Adversarial Networks(GANs) are powerful generative models on numerous tasks and datasets but are also known for their training instability and mode collapse. The latter is because the optimal transportation map is discontinuous, but DNNs can only approximate continuous ones. One way to solve the problem is to introduce multiple discriminators or generators. However, their impacts are limited because the cost function of each component is the same. That is, they are homogeneous. Indoi:10.24963/ijcai.2022/164 fatcat:j4ti2t6rivcdjkb33qrtymygs4