Approaching an unknown communication system by latent space exploration and causal inference

This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this method yields insights for model interpretability. With this, we can test for what properties of unknown data the model encodes as meaningful, using it to glean insight into …

call causal inference communication cs.lg cs.sd data deep generative models eess.as exploration generative generative models inference manipulation methodology paper space stat.ml unsupervised values variables