CVPR 2023 – StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

In this episode we discuss StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
by Yuqian Fu, Yu Xie, Yanwei Fu, Yu-Gang Jiang. The paper proposes a novel model-agnostic meta Style Adversarial training (StyleAdv) method for Cross-Domain Few-Shot Learning (CD-FSL), a task that aims to transfer prior knowledge learned on a source dataset to novel target datasets. This is achieved by using a style adversarial attack method that synthesizes “virtual” and “hard” adversarial styles for model training, gradually making the model robust to visual styles and boosting its generalization ability. The proposed method achieves state-of-the-art results on eight various target datasets, whether built upon ResNet or ViT. Code is available on GitHub.