MetaDefa: Meta-learning based on Domain Enhancement and Feature
  Alignment for Single Domain Generalization
Can Sun Hao Zheng Zhigang Hu Liu Yang Meiguang Zheng Bo Xu
The single domain generalization(SDG) based on meta-learning has emerged as
an effective technique for solving the domain-shift problem. However, the
inadequate match of data distribution between source and augmented domains and
difficult separation of domain-invariant features from domain-related features
make SDG model hard to achieve great generalization. Therefore, a novel
meta-learning method based on domain enhancement and feature alignment
(MetaDefa) is proposed to improve the model generalization performance. First,
the background substitution and visual corruptions techniques are used to
generate diverse and effective augmented domains. Then, the multi-channel
feature alignment module based on class activation maps and class agnostic
activation maps is designed to effectively extract adequate transferability
knowledge. In this module, domain-invariant features can be fully explored by
focusing on similar target regions between source and augmented domains feature
space and suppressing the feature representation of non-similar target regions.
Extensive experiments on two publicly available datasets show that MetaDefa has
significant generalization performance advantages in unknown multiple target
domains.