You will be given a definition of a task first, then some input of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

In this paper, we are interested in two seemingly different concepts: extit{adversarial training} and extit{generative adversarial networks (GANs)}. Particularly, how these techniques work to improve each other. To this end, we analyze the limitation of adversarial training as a defense method, starting from questioning how well the robustness of a model can generalize. Then, we successfully improve the generalizability via data augmentation by the ''fake'' images sampled from generative adversarial network. After that, we are surprised to see that the resulting robust classifier leads to a better generator, for free. We intuitively explain this interesting phenomenon and leave the theoretical analysis for future work.
Motivated by these observations, we propose a system that combines generator, discriminator, and adversarial attacker together in a single network. After end-to-end training and fine tuning, our method can simultaneously improve the robustness of classifiers, measured by accuracy under strong adversarial attacks, and the quality of generators, evaluated both aesthetically and quantitatively. In terms of the classifier, we achieve better robustness than the state-of-the-art adversarial training algorithm proposed in (Madry extit{et al.}, 2017), while our generator achieves competitive performance compared with SN-GAN (Miyato and Koyama, 2018).
Output:
We found adversarial training not only speeds up the GAN training but also increases the image quality