In this paper we propose an implement a general convolutional neural network
(CNN) building framework for designing real-time CNNs. We validate our models
by creating a real-time vision system which accomplishes the tasks of face
detection, gender classification and emotion classification simultaneously in
one blended step using our proposed CNN architecture. After presenting the
details of the training procedure setup we proceed to evaluate on standard
benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66%
in the FER-2013 emotion dataset. Along with this we also introduced the very
recent real-time enabled guided back-propagation visualization technique.
Guided back-propagation uncovers the dynamics of the weight changes and
evaluates the learned features. We argue that the careful implementation of
modern CNN architectures, the use of the current regularization methods and the
visualization of previously hidden features are necessary in order to reduce
the gap between slow performances and real-time architectures. Our system has
been validated by its deployment on a Care-O-bot 3 robot used during
RoboCup@Home competitions. All our code, demos and pre-trained architectures
have been released under an open-source license in our public repository.

本文提出并实现了一个通用的卷积神经网络（CNN）构建框架，用于设计实时 CNN。我们通过创建一个实时视觉系统来验证我们的模型，在一个混合步骤中同时完成面部检测、性别分类和情感分类的任务。我们提供了训练过程设置的详细信息，并在标准基准集上进行了评估，报告了 IMDB 性别数据集中 96% 和 FER-2013 情感数据集中 66% 的准确性。此外，我们还介绍了最近的实时启用的引导反向传播可视化技术。引导反向传播揭示了权重变化的动态，并评估了学习到的特征。我们认为，仔细实施现代 CNN 架构、使用当前的正则化方法和可视化以前隐藏的特征对于缩小慢速性能和实时架构之间的差距是必要的。我们的系统通过在 RoboCup＠Home 比赛中使用的 Care-O-bot 3 机器人上部署进行了验证。我们的所有代码、演示和预训练的架构都在我们的公共存储库中以开源许可证发布。