On-Device Domain Generalization
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We present a systematic study of domain generalization (DG) for tiny neural networks, a problem that is critical to on-device machine learning applications but has been overlooked in the literature where research has been focused on large models only. Tiny neural networks have much fewer parameters and lower complexity, and thus should not be trained the same way as their large counterparts for DG applications. We find that knowledge distillation is a strong candidate for solving the problem: it outperforms state-of-the-art DG methods that were developed using large models with a large margin. Moreover, we observe that the teacher-student performance gap on test data with domain shift is bigger than that on in-distribution data. To improve DG for tiny neural networks without increasing the deployment cost, we propose a simple idea called out-of-distribution knowledge distillation (OKD), which aims to teach the student how the teacher handles (synthetic) out-of-distribution data and is proved to be a promising framework for solving the problem. We also contribute a scalable method of creating DG datasets, called DOmain Shift in COntext (DOSCO), which can be applied to broad data at scale without much human effort. Code and models are released at <https://github.com/KaiyangZhou/on-device-dg>.
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