Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks

• URL: http://arxiv.org/abs/2106.12900v1
• Date: Tue, 22 Jun 2021 06:31:16 GMT
• Title: Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks
• Authors: Fan Liu, Shuyu Zhao, Xuelong Dai, Bin Xiao
• Abstract summary: This paper proposed a meta-learning method on the adversarially robust neural network called Long-term Cross Adversarial Training (LCAT) Due to cross-adversarial training, LCAT only needs half of the adversarial training epoch than AQ, resulting in a low adversarial training epoch. Experiment results show that LCAT achieves superior performance both on the clean and adversarial few-shot classification accuracy.
• Score: 10.058068783476598
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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