A large body of recent work has identified transformations in the latent
spaces of generative adversarial networks (GANs) that consistently and
interpretably transform generated images. But existing techniques for
identifying these transformations rely on either a fixed vocabulary of
pre-specified visual concepts, or on unsupervised disentanglement techniques
whose alignment with human judgments about perceptual salience is unknown. This
paper introduces a new method for building open-ended vocabularies of primitive
visual concepts represented in a GAN's latent space. Our approach is built from
three components: (1) automatic identification of perceptually salient
directions based on their layer selectivity; (2) human annotation of these
directions with free-form, compositional natural language descriptions; and (3)
decomposition of these annotations into a visual concept vocabulary, consisting
of distilled directions labeled with single words. Experiments show that
concepts learned with our approach are reliable and composable -- generalizing
across classes, contexts, and observers, and enabling fine-grained manipulation
of image style and content.

本文介绍了一种新的方法，针对生成对抗网络（GAN）中的潜在空间建立无限制的基元视觉概念词汇表，该方法通过三个组件实现：(1) 基于层选择自动识别知觉显著方向；(2) 人工注释这些方向以自由形式的组成自然语言描述，以及 (3) 将这些注释分解为视觉概念词汇表，由单词标记的精简方向组成。实验表明，使用我们的方法学习的概念是可靠和可组合的 - 概念可以跨越类别、上下文和观察者进行概括，并且可以实现对图像风格和内容的精细操作。