StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the source dataset to novel target datasets. The CD-FSL task is especially challenged by the huge domain gap between different datasets. Critically, such a domain gap …