Abstract
Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel approach to the problem of domain generalization in the context of deep learning. The proposed method is evaluated on different datasets in various problems, i.e. (i) digit recognition on MNIST, SVHN and MNIST-M, (ii) face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE, and (iii) pedestrian recognition on RGB and Thermal image datasets. The experimental results show that our proposed method consistently improves the performance accuracy. It can be also easily incorporated with any other CNN frameworks within an end-to-end deep network design for object detection and recognition problems to improve their performance.
Abstract (translated)
跨领域识别最近成为研究界的一个活跃话题。然而，它在新的未知领域的识别问题中却被忽视了。在这种情况下，交付的深层网络模型无法更新、调整或微调。因此，不能应用最新的深度学习技术，如：领域适应、特征转移和微调。本文提出了一种在深度学习背景下解决领域泛化问题的新方法。对不同数据集在不同问题上的识别方法进行了评价，包括：（i）mnist、svhn和mnist-m上的数字识别；（ii）扩展yale-b、cmu-pie和cmu-mpie上的人脸识别；（iii）RGB和热图像数据集上的行人识别。实验结果表明，本文提出的方法能持续提高系统的性能精度。它还可以很容易地与任何其他CNN框架结合在一个端到端的深度网络设计中，用于对象检测和识别问题，以提高它们的性能。
URL
https://arxiv.org/abs/1905.13040
