Domain Generalization via Universal Non-volume Preserving Models
This addresses the problem of deploying deep learning models in new, unseen domains for researchers and practitioners, though it appears incremental as it builds on existing CNN frameworks.
The paper tackles domain generalization in deep learning by proposing a novel approach that improves recognition accuracy across unseen domains without requiring model updates, as demonstrated on digit, face, and pedestrian recognition datasets with consistent performance gains.
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 performance accuracy. It can also be 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.