Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks
Abstract
Meta-learning model can quickly adapt to new tasks using few-shot labeled data. However, despite achieving good generalization on few-shot classification tasks, it is still challenging to improve the adversarial robustness of the meta-learning model in few-shot learning. Although adversarial training (AT) methods such as Adversarial Query (AQ) can improve the adversarially robust performance of meta-learning models, AT is still computationally expensive training. On the other hand, meta-learning models trained with AT will drop significant accuracy on the original clean images. This paper proposed a meta-learning method on the adversarially robust neural network called Long-term Cross Adversarial Training (LCAT). LCAT will update meta-learning model parameters cross along the natural and adversarial sample distribution direction with long-term to improve both adversarial and clean few-shot classification accuracy. Due to cross-adversarial training, LCAT only needs half of the adversarial training epoch than AQ, resulting in a low adversarial training computation. Experiment results show that LCAT achieves superior performance both on the clean and adversarial few-shot classification accuracy than SOTA adversarial training methods for meta-learning models.
Cite
@article{arxiv.2106.12900,
title = {Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks},
author = {Fan Liu and Shuyu Zhao and Xuelong Dai and Bin Xiao},
journal= {arXiv preprint arXiv:2106.12900},
year = {2021}
}
Comments
Accepted by the ICML 2021 Workshop on A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning(https://openreview.net/group?id=ICML.cc/2021/Workshop/AML)