论文标题
CPPN2GAN：结合大规模图案生成的组成模式产生网络和gan
CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation
论文作者
论文摘要
事实证明，生成的对抗网络（GAN）是一种强大的间接基因型对进化搜索的映射，但它们有局限性。特别是，GAN输出并不能扩展到任意维度，并且没有明显的方法将多个GAN输出组合为一个有凝聚力的整体，这在许多领域（例如，视频游戏水平的产生）很有用。游戏水平通常由几个部分组成，有时直接重复或随着变化而组织为引人入胜的模式。可以通过组成模式产生网络（CPPN）产生这种模式。具体而言，CPPN可以定义潜在矢量gan输入作为几何形状的函数，该几何形状提供了一种将gAN输出到完整级别的水平段的方法。这种新的CPPN2GAN方法在Super Mario Bros.和The Zelda的传奇中都得到了验证。具体而言，通过MAP-ELITE的发散搜索表明，CPPN2GAN可以更好地覆盖可能的水平。所得级别的布局也更加凝聚力和美学一致。
Generative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels. Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern. Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). Specifically, a CPPN can define latent vector GAN inputs as a function of geometry, which provides a way to organize level segments output by a GAN into a complete level. This new CPPN2GAN approach is validated in both Super Mario Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites demonstrates that CPPN2GAN can better cover the space of possible levels. The layouts of the resulting levels are also more cohesive and aesthetically consistent.
