Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Adversarial training provides a principled approach for training robust neural networks.
From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem.
The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to generate adversarial samples.
Unfortunately, such a minmax problem is very difficult to solve due to the lack of convex-concave structure.
This work proposes a new adversarial training method based on a general learning-to-learn framework.
Specifically, instead of applying the existing hand-design algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network.
At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer.
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
Our experiments demonstrate that our proposed method significantly outperforms existing adversarial training methods on CIFAR-10 and CIFAR-100 datasets.
Don't know how to optimize? Then just learn to optimize!