  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}
