  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.
