论文标题
通过域混合的异质域概括
Heterogeneous Domain Generalization via Domain Mixup
论文作者
论文摘要
深卷积神经网络（DCNN）的主要缺点之一是它们缺乏概括能力。在这项工作中，我们专注于异质域概括的问题，该问题旨在提高不同任务之间的概括能力，即如何学习具有多个域数据的DCNN模型，以便可以将训练的特征提取器推广到支持新型目标域中新型类别的识别。为了解决这个问题，我们通过将跨多个源域的样品与两种不同的采样策略混合在一起，提出了一种新型的异质域概括方法。我们基于视觉十项债券基准的实验结果证明了我们提出的方法的有效性。该代码在\ url {https://github.com/wyf0912/mixall}中发布
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}
