Heterogeneous Domain Generalization via Domain Mixup

Abstract

One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain generalization which aims to improve the generalization capability across different tasks, which is, how to learn a DCNN model with multiple domain data such that the trained feature extractor can be generalized to supporting recognition of novel categories in a novel target domain. To solve this problem, we propose a novel heterogeneous domain generalization method by mixing up samples across multiple source domains with two different sampling strategies. Our experimental results based on the Visual Decathlon benchmark demonstrates the effectiveness of our proposed method. The code is released in \url{https://github.com/wyf0912/MIXALL}

References in corpus (3)

• On the Convergence of Adam and Beyond
• Domain Generalization via Invariant Feature Representation
• Feature-Critic Networks for Heterogeneous Domain Generalization

Cited by in corpus (6)

• In Search of Lost Domain Generalization
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
• Out-of-Distribution Generalization Analysis via Influence Function
• Open Domain Generalization with Domain-Augmented Meta-Learning
• Domain Generalization for Session-Independent Brain-Computer Interface
• Explainability-aided Domain Generalization for Image Classification