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.

本文提出了一种基于长期交叉对抗训练(LCAT)的元学习方法，在自然和对抗样本分布方向上进行跨更新，以提高元学习模型的对抗性和干净的Few-shot分类准确性，实验结果表明，LCAT在对抗样本上和干净样本上的Few-shot识别准确性上都优于目前最先进的元学习模型的对抗训练方法。

长期交叉对抗训练: 一种鲁棒元学习方法用于少样本分类任务