papers AI Learner
The Github is limit! Click to go to the new site.

Recognition in Unseen Domains: Domain Generalization via Universal Non-volume Preserving Models

2019-05-28
Thanh-Dat Truong, Chi Nhan Duong, Khoa Luu, Minh-Triet Tran

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 by Google)
URL

http://arxiv.org/abs/1905.13040

PDF

http://arxiv.org/pdf/1905.13040


Similar Posts

Comments