论文标题
网络弯曲：深层生成模型的表现性操纵
Network Bending: Expressive Manipulation of Deep Generative Models
论文作者
论文摘要
我们介绍了一个新的框架，用于操纵和与我们称为网络弯曲的深层生成模型进行交互。我们提出了一组全面的确定性转换，可以将它们插入不同的层中，并在训练有素的生成神经网络的计算图中插入并在推理期间应用。此外，我们提出了一种新型算法，用于根据其空间激活图分析深层生成模型和聚类特征。这允许以无监督的方式根据空间相似性将功能分组在一起。这导致对一组特征的有意义操纵，这些功能与生成的图像的一系列具有广泛的语义显着特征相对应。我们概述了此框架，展示了我们在几个图像数据集中训练的最先进的深层生成模型上的结果。我们展示了它如何允许对生成过程的语义上有意义的方面进行直接操纵以及允许广泛的表现性结果。
We introduce a new framework for manipulating and interacting with deep generative models that we call network bending. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated images. We outline this framework, demonstrating our results on state-of-the-art deep generative models trained on several image datasets. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.
