  In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural
networks which learn a low dimensional representation of data which are
additionally enriched with a desired structure in this low dimensional space.
While traditional Autoencoders have proven to structure data naturally they
fail to discover semantic structure that is hard to recognize in the raw data.
The SAE solves the problem by enhancing a traditional Autoencoder using weak
supervision to form a structured latent space. In the experiments we
demonstrate, that the structured latent space allows for a much more efficient
data representation for further tasks such as classification for sparsely
labeled data, an efficient choice of data to label, and morphing between
classes. To demonstrate the general applicability of our method, we show
experiments on the benchmark image datasets MNIST, Fashion-MNIST, DeepFashion2
and on a dataset of 3D human shapes.
