Most existing methods for conditional image synthesis are only able to
generate a single plausible image for any given input, or at best a fixed
number of plausible images. In this paper, we focus on the problem of
generating images from semantic segmentation maps and present a simple new
method that can generate an arbitrary number of images with diverse appearance
for the same semantic layout. Unlike most existing approaches which adopt the
GAN framework, our method is based on the recently introduced Implicit Maximum
Likelihood Estimation (IMLE) framework. Compared to the leading approach, our
method is able to generate more diverse images while producing fewer artifacts
despite using the same architecture. The learned latent space also has sensible
structure despite the lack of supervision that encourages such behaviour.
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本文研究了条件图像合成中的多样性问题，提出了基于 IMLE 框架的新方法，用于从语义分割映射生成具有不同外观的图像，并能够生成更多样化的图像，同时产生更少的伪影，具有合理的潜在空间结构。