Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning

no code implementations • 19 Jan 2023 • Shuzhen Rao, Jun Huang, Zengming Tang

Motivated by the observation that the domain shift between training tasks and target tasks usually can reflect in their style variation, we propose Task Augmented Meta-Learning (TAML) to conduct style transfer-based task augmentation to improve the domain generalization ability.

cross-domain few-shot learning Domain Generalization +1