Deep generative models can emulate the perceptual properties of complex image
datasets, providing a latent representation of the data. However, manipulating
such representation to perform meaningful and controllable transformations in
the data space remains challenging without some form of supervision. While
previous work has focused on exploiting statistical independence to disentangle
latent factors, we argue that such requirement is too restrictive and propose
instead a non-statistical framework that relies on counterfactual manipulations
to uncover a modular structure of the network composed of disentangled groups
of internal variables. Experiments with a variety of generative models trained
on complex image datasets show the obtained modules can be used to design
targeted interventions. This opens the way to applications such as
computationally efficient style transfer and the automated assessment of
robustness to contextual changes in pattern recognition systems.

本研究提出了一种非统计框架，它使用反事实干预来揭示由解缠组成的网络的模块化结构，以便操纵潜在表示以执行有意义和可控的变换。通过在复杂图像数据集上训练各种生成模型进行实验，表明所获得的模块可用于设计目标干预，从而在风格转换和自动评估对环境变化的稳健性等领域开展应用。