学习单域通用性

Mar, 2020

Learning to Learn Single Domain Generalization

Fengchun Qiao, Long Zhao, Xi Peng

TL;DR本篇文章介绍了一种名为对抗域增强的方法，用于在单一训练域情况下提高模型的泛化性能，通过使用元学习方案和WAE来松弛最坏情况限制，并在多个基准数据集上广泛实验，验证了该方法对于缓解单一域泛化问题的优越表现。

Abstract

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named →