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.
We investigate the modularity of deep generative models. The paper provides a way to investigate the modular structure of the deep generative model, with the key concept to distribute over channels of generator architectures.