Self-Supervised Learning of Color Constancy
This addresses the developmental mechanism of color constancy in humans, offering a plausible explanation through computational modeling, but it is incremental as it applies existing self-supervised methods to a new domain.
The study tackled the problem of how color constancy develops in the visual system by showing that it emerges in a neural network trained with a self-supervised invariance learning objective, resulting in representations largely invariant to illumination changes.
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.