Latent-space interpolation is commonly used to demonstrate the generalization ability of deep latent variable models. Various algorithms have been proposed to calculate the best trajectory between two encodings in the latent space. In this work, we show how data labeled with semantically continuous attributes can be utilized to conduct a quantitative evaluation of latent-space interpolation algorithms, for variational autoencoders. Our framework can be used to complement the standard qualitative comparison, and also enables evaluation for domains (such as graph) in which the visualization is difficult. Interestingly, our experiments reveal that the superiority of interpolation algorithms could be domain-dependent. While normalised interpolation works best for the image domain, spherical linear interpolation achieves the best performance in the graph domain. Next, we propose a simple-yet-effective method to restrict the latent space via a bottleneck structure in the encoder. We find that all interpolation algorithms evaluated in this work can benefit from this restriction. Finally, we conduct interpolation-aware training with the labeled attributes, and show that this explicit supervision can improve the interpolation performance.

翻译：通常使用隐性空间内插法来显示深潜变量模型的通用能力。 已经提出了各种算法来计算潜质空间中两个编码之间的最佳轨迹。 在这项工作中, 我们展示了如何利用带有隐性连续属性标签的数据对潜空内插算法进行定量评估, 用于变式自动对立。 我们的框架可以用来补充标准的质量比较, 并且也可以用来评价难以视觉化的领域( 如图) 。 有趣的是, 我们的实验显示, 内插算法的优越性可能是取决于域的。 虽然普通化的内插法对图像域最有效, 球形线内插法则在图形域中取得最佳性能。 接下来, 我们提出一种简单而有效的方法, 通过编码器的瓶颈结构限制潜值空间。 我们发现, 这项工作中评价的所有内插法都可以从这一限制中受益 。 最后, 我们用标签属性进行内插法培训, 并表明这种明确的监督可以改进内插法的性能。