  Vector quantised variational autoencoders (VQ-VAE) are characterised by three
main components: 1) encoding visual data, 2) assigning $k$ different vectors in
the so-called embedding space, and 3) decoding the learnt features. While
images are often represented in RGB colour space, the specific organisation of
colours in other spaces also offer interesting features, e.g. CIE L*a*b*
decorrelates chromaticity into opponent axes. In this article, we propose
colour space conversion, a simple quasi-unsupervised task, to enforce a network
learning structured representations. To this end, we trained several instances
of VQ-VAE whose input is an image in one colour space, and its output in
another, e.g. from RGB to CIE L*a*b* (in total five colour spaces were
considered). We examined the finite embedding space of trained networks in
order to disentangle the colour representation in VQ-VAE models. Our analysis
suggests that certain vectors encode hue and others luminance information. We
further evaluated the quality of reconstructed images at low-level using
pixel-wise colour metrics, and at high-level by inputting them to image
classification and scene segmentation networks. We conducted experiments in
three benchmark datasets: ImageNet, COCO and CelebA. Our results show, with
respect to the baseline network (whose input and output are RGB), colour
conversion to decorrelated spaces obtains 1-2 Delta-E lower colour difference
and 5-10% higher classification accuracy. We also observed that the learnt
embedding space is easier to interpret in colour opponent models.
