Learning to Learn Single Domain Generalization

CVPR 2020 Fengchun QiaoLong ZhaoXi Peng

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Out-of-Distribution (OOD) generalization problem... (read more)

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