Heterogeneous Domain Generalization via Domain Mixup

• URL: http://arxiv.org/abs/2009.05448v1
• Date: Fri, 11 Sep 2020 13:53:56 GMT
• Title: Heterogeneous Domain Generalization via Domain Mixup
• Authors: Yufei Wang (1 and 2), Haoliang Li (2), and Alex C. Kot (2)((1) University of Electronic Science and Technology of China, China, (2) Nanyang Technological University, Singapore)
• Abstract summary: One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. We propose a novel heterogeneous domain generalization method by mixing up samples across multiple source domains. Our experimental results based on the Visual Decathlon benchmark demonstrates the effectiveness of our proposed method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}

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