Heterogeneous Domain Generalization via Domain Mixup
This addresses the lack of generalization capability in DCNNs for computer vision tasks, but appears incremental as it builds on existing domain generalization techniques.
The paper tackles the problem of improving generalization across different tasks in deep convolutional neural networks by proposing a heterogeneous domain generalization method using domain mixup with two sampling strategies, achieving effectiveness demonstrated on the Visual Decathlon benchmark.
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}