Self-Supervised Learning of Color Constancy
Markus R. Ernst Francisco M. López Arthur Aubret Roland W. Fleming Jochen Triesch
Color constancy (CC) describes the ability of the visual system to perceive
an object as having a relatively constant color despite changes in lighting
conditions. While CC and its limitations have been carefully characterized in
humans, it is still unclear how the visual system acquires this ability during
development. Here, we present a first study showing that CC develops in a
neural network trained in a self-supervised manner through an invariance
learning objective. During learning, objects are presented under changing
illuminations, while the network aims to map subsequent views of the same
object onto close-by latent representations. This gives rise to representations
that are largely invariant to the illumination conditions, offering a plausible
example of how CC could emerge during human cognitive development via a form of
self-supervised learning.