Papers › Artificial Color Constancy via GoogLeNet with Angular Loss Function
Artificial Color Constancy via GoogLeNet with Angular Loss Function
Oleksii Sidorov
Color Constancy is the ability of the human visual system to perceive colors unchanged independently of the illumination. Giving a machine this feature will be beneficial in many fields where chromatic information is used. Particularly, it significantly improves scene understanding and object recognition. In this paper, we propose transfer learning-based algorithm, which has two main features: accuracy higher than many state-of-the-art algorithms and simplicity of implementation. Despite the fact that GoogLeNet was used in the experiments, given approach may be applied to any CNN. Additionally, we discuss design of a new loss function oriented specifically to this problem, and propose a few the most suitable options.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections