This paper proposes a novel hue-like angular parameter to model the structure of deep convolutional neural network (CNN) activation space, referred to as the {\em activation hue}, for the purpose of regularizing models for more effective learning. The activation hue generalizes the notion of color hue angle in standard 3-channel RGB intensity space to $N$-channel activation space. A series of observations based on nearest neighbor indexing of activation vectors with pre-trained networks indicate that class-informative activations are concentrated about an angle $\theta$ in both the $(x,y)$ image plane and in multi-channel activation space. A regularization term in the form of hue-like angular $\theta$ labels is proposed to complement standard one-hot loss. Training from scratch using combined one-hot + activation hue loss improves classification performance modestly for a wide variety of classification tasks, including ImageNet.

翻译：本文提出一种新型的似色调角度参数，用于刻画深度卷积神经网络（CNN）激活空间的结构，称为"激活色调"，其目的是对模型进行正则化以实现更有效的学习。激活色调将标准三通道RGB强度空间中颜色色调角的概念推广至$N$通道激活空间。基于预训练网络对激活向量进行最近邻索引的一系列观测表明，类别信息性激活在$(x,y)$图像平面与多通道激活空间中均集中于角度$\theta$附近。本文提出一种以似色调角度$\theta$标签形式存在的正则化项，用于补充标准独热损失。在包括ImageNet在内的多种分类任务中，采用联合独热损失与激活色调损失从头训练模型，可适度提升分类性能。