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Uncertainty-guided Model Generalization to Unseen Domains (CVPR 2021)

This repository holds the Pytorch implementation of Uncertainty-guided Model Generalization to Unseen Domains by Fengchun Qiao and Xi Peng.

Introduction

We study a worst-case scenario in generalization: Out-of-domain generalization from a single source. The goal is to learn a robust model from a single source and expect it to generalize over many unknown distributions. This challenging problem has been seldom investigated while existing solutions suffer from various limitations. In this paper, we propose a new solution. The key idea is to augment the source capacity in both input and label spaces, while the augmentation is guided by uncertainty assessment.

Prerequisites

This package has the following requirements:

• Python 3.6
• Pytorch 1.1.0
• MetaNN 0.1.5
• Scipy 1.2.1

Training

Run the following command:

python main.py

Citation

If you find our code useful in your research, please consider citing:

@InProceedings{Qiao_2021_CVPR,
    author    = {Qiao, Fengchun and Peng, Xi},
    title     = {Uncertainty-Guided Model Generalization to Unseen Domains},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {6790-6800}
}