ABSTRACT Color constancy is the ability of the human visual system to perceive colors unchanged independently of 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 article, we propose a 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, the given approach may be applied to any convolutional neural networks. Additionally, we discuss the design of a new loss function oriented specifically to this problem and propose a few of the most suitable options.

中文翻译：

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通过具有角度损失函数的 GoogLeNet 实现人工颜色恒常性

摘要 颜色恒常性是人类视觉系统感知颜色不变的能力，不受光照影响。在使用彩色信息的许多领域中，赋予机器此功能将是有益的。特别是，它显着提高了场景理解和物体识别。在本文中，我们提出了一种基于迁移学习的算法，它具有两个主要特点：精度高于许多最先进的算法和实现简单。尽管在实验中使用了 GoogLeNet，但给定的方法可以应用于任何卷积神经网络。此外，我们讨论了专门针对此问题的新损失函数的设计，并提出了一些最合适的选项。