Recognition in Unseen Domains: Domain Generalization via Universal Non-volume Preserving Models.
Year: 2019
Type: preprint
Abstract: Recognition across domains has recently become an active topic in theresearch 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2946907965