Rayleigh EigenDirections（RED）：多维要素的GAN潜在空间遍历
Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features
摘要：
我们提出了一种在深度生成模型的潜在空间中寻找路径的方法，该方法可以最大限度地改变一组图像特征，同时保持其他特征不变。至关重要的是，与过去的遍历方法不同，我们的方法可以操纵图像的多维特征，例如指定区域内的面部身份和像素。我们的方法是原则性的，概念上简单：最佳遍历方向的选择，通过最大限度地提高一个功能集的差异变化，使另一个集的变化可以忽略不计。我们证明了这个问题几乎等价于一个瑞利商最大化，并提供了一个封闭形式的解决方案，它的基础上求解广义特征值方程。我们使用相应的最佳方向，我们称之为瑞利本征方向（RED）的重复计算，在潜在空间中生成适当的弯曲路径。我们使用StyleGAN2在两个图像域上对我们的方法进行了经验评估：面部和客厅。我们表明，我们的方法是能够控制各种多维功能的范围内以前的潜在的空间遍历方法：人脸识别，空间频带，一个区域内的像素，和一个对象的外观和位置。我们的工作表明，丰富的机会在于潜在空间的几何和语义的本地分析。
英文摘要：
We present a method for finding paths in a deep generative model's latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike past traversal approaches, ours can manipulate multidimensional features of an image such as facial identity and pixels within a specified region. Our method is principled and conceptually simple: optimal traversal directions are chosen by maximizing differential changes to one feature set such that changes to another set are negligible. We show that this problem is nearly equivalent to one of Rayleigh quotient maximization, and provide a closed-form solution to it based on solving a generalized eigenvalue equation. We use repeated computations of the corresponding optimal directions, which we call Rayleigh EigenDirections (REDs), to generate appropriately curved paths in latent space. We empirically evaluate our method using StyleGAN2 on two image domains: faces and living rooms. We show that our method is capable of controlling various multidimensional features out of the scope of previous latent space traversal methods: face identity, spatial frequency bands, pixels within a region, and the appearance and position of an object. Our work suggests that a wealth of opportunities lies in the local analysis of the geometry and semantics of latent spaces.
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