Tuesday May 23, 2023
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.
Comments (0)
To leave or reply to comments, please download free Podbean or
No Comments
To leave or reply to comments,
please download free Podbean App.