Title: Composition and decomposition of GANs,
Abstract: In this work, we propose a composition/decomposition framework for
adversarially training generative models on composed data - data where each
sample can be thought of as being constructed from a fixed number of
components. In our framework, samples are generated by sampling components from
component generators and feeding these components to a composition function
which combines them into a "composed sample". This compositional training
approach improves the modularity, extensibility and interpretability of
Generative Adversarial Networks (GANs) - providing a principled way to
incrementally construct complex models out of simpler component models, and
allowing for explicit "division of responsibility" between these components.
Using this framework, we define a family of learning tasks and evaluate their
feasibility on two datasets in two different data modalities (image and text).
Lastly, we derive sufficient conditions such that these compositional
generative models are identifiable. Our work provides a principled approach to
building on pre-trained generative models or for exploiting the compositional
nature of data distributions to train extensible and interpretable models.