title: Tinkering with black boxes: counterfactuals uncover modularity in generative models
abstract: Deep generative models such as Generative Adversarial Networks (GANs) and
  Variational Auto-Encoders (VAEs) are important tools to capture and investigate
  the properties of complex empirical data. However, the complexity of their inner
  elements makes their functionment challenging to assess and modify. In this
  respect, these architectures behave as black box models. In order to better
  understand the function of such networks, we analyze their modularity based on
  the counterfactual manipulation of their internal variables. Our experiments on the
  generation of human faces with VAEs and GANs support that modularity between
  activation maps distributed over channels of generator architectures is achieved
  to some degree, can be used to better understand how these systems operate and allow meaningful transformations of the generated images without further training.
  erate and edit the content of generated images.
authors: Michel Besserve Remy Sun Bernhard Schoelkopf
hIndex: Michel Besserve 11