  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.
