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Recent advances in conditional image generation tasks, such as image-to-imagetranslation and image inpainting, are largely accounted to the success ofconditional GAN models, which are often optimized by the joint use of the GANloss with the reconstruction loss. However, we reveal that this training recipeshared by almost all existing methods causes one critical side effect: lack ofdiversity in output samples. In order to accomplish both training stability andmultimodal output generation, we propose novel training schemes with a new setof losses named moment reconstruction losses that simply replace thereconstruction loss. We show that our approach is applicable to any conditionalgeneration tasks by performing thorough experiments on image-to-imagetranslation, super-resolution and image inpainting using Cityscapes and CelebAdataset. Quantitative evaluations also confirm that our methods achieve a greatdiversity in outputs while retaining or even improving the visual fidelity ofgenerated samples.

Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation