  This report to our stage 2 submission to the NeurIPS 2019 disentanglement
challenge presents a simple image preprocessing method for learning
disentangled latent factors. We propose to train a variational autoencoder on
regionally aggregated feature maps obtained from networks pretrained on the
ImageNet database, utilizing the implicit inductive bias contained in those
features for disentanglement. This bias can be further enhanced by explicitly
fine-tuning the feature maps on auxiliary tasks useful for the challenge, such
as angle, position estimation, or color classification. Our approach achieved
the 2nd place in stage 2 of the challenge. Code is available at
https://github.com/mseitzer/neurips2019-disentanglement-challenge.
