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With the rapidly growing model complexity and data volume, training deepgenerative models (DGMs) for better performance has becoming an increasinglymore important challenge. Previous research on this problem has mainly focusedon improving DGMs by either introducing new objective functions or designingmore expressive model architectures. However, such approaches often introducesignificantly more computational and/or designing overhead. To resolve suchissues, we introduce in this paper a generic framework called {\emgenerative-model inference} that is capable of enhancing pre-trained GANseffectively and seamlessly in a variety of application scenarios. Our basicidea is to efficiently infer the optimal latent distribution for the givenrequirements using Wasserstein gradient flow techniques, instead of re-trainingor fine-tuning pre-trained model parameters. Extensive experimental results onapplications like image generation, image translation, text-to-imagegeneration, image inpainting, and text-guided image editing suggest theeffectiveness and superiority of our proposed framework.

A Generic Approach for Enhancing GANs by Regularized Latent Optimization