# Structuring Autoencoders

## TL;DR

This paper introduces Structuring AutoEncoders (SAE), a neural network model that learns low-dimensional, semantically structured representations of data using weak supervision, improving tasks like classification and data morphing.

## Contribution

The paper presents a novel autoencoder variant that incorporates weak supervision to produce structured latent spaces, enhancing data representation and task performance.

## Key findings

- Structured latent space improves classification accuracy.
- Efficient data labeling through the structured representation.
- Effective morphing between classes demonstrated.

## Abstract

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.