2021

Domain Generalization with MixStyle

Zhou, Kaiyang, Yang, Yongxin, Qiao, Yu et al.

Understand

Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains.

Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain.
In this paper, a novel approach is proposed based on probabilistically mixing instance-level feature statistics of training samples across source domains.
Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e.g., photo vs.~sketch images).

Built on

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