Self-Supervised Learning of Color Constancy

Autor:	Ernst, Markus R., López, Francisco M., Aubret, Arthur, Fleming, Roland W., Triesch, Jochen
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 5 figures, submitted to the IEEE International Conference on Development and Learning (ICDL 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.08127 Zobrazit plný text záznamu View this record from Arxiv