# Domain Generalization via Universal Non-volume Preserving Models

## TL;DR

This paper introduces a novel deep learning approach for domain generalization that enhances recognition accuracy across unseen domains without requiring model updates or fine-tuning.

## Contribution

It proposes a universal non-volume preserving model that improves domain generalization in deep learning, applicable to various recognition tasks and datasets.

## Key findings

- Consistently improves recognition accuracy across multiple datasets.
- Easily integrated with existing CNN frameworks.
- Effective in digit, face, and pedestrian recognition tasks.

## 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 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.