  A challenge of the computer vision community is to understand the semantics
of an image, in order to allow image reconstruction based on existing
high-level features or to better analyze (semi-)labelled datasets. Towards
addressing this challenge, this paper introduces a method, called
Occlusion-based Latent Representations (OLR), for converting image labels to
meaningful representations that capture a significant amount of data semantics.
Besides being informational rich, these representations compose a disentangled
low-dimensional latent space where each image label is encoded into a separate
vector. We evaluate the quality of these representations in a series of
experiments whose results suggest that the proposed model can capture data
concepts and discover data interrelations.
