  Disentangled representations can be useful in many downstream tasks, help to
make deep learning models more interpretable, and allow for control over
features of synthetically generated images that can be useful in training other
models that require a large number of labelled or unlabelled data. Recently,
flow-based generative models have been proposed to generate realistic images by
directly modeling the data distribution with invertible functions. In this
work, we propose a new flow-based generative model framework, named GLOWin,
that is end-to-end invertible and able to learn disentangled representations.
Feature disentanglement is achieved by factorizing the latent space into
components such that each component learns the representation for one
generative factor. Comprehensive experiments have been conducted to evaluate
the proposed method on a public brain tumor MR dataset. Quantitative and
qualitative results suggest that the proposed method is effective in
disentangling the features from complex medical images.
