Recent work argues that robust training requires substantially larger
datasets than those required for standard classification. On CIFAR-10 and
CIFAR-100, this translates into a sizable robust-accuracy gap between models
trained solely on data from the original training set and those trained with
additional data extracted from the "80 Million Tiny Images" dataset (TI-80M).
In this paper, we explore how generative models trained solely on the original
training set can be leveraged to artificially increase the size of the original
training set and improve adversarial robustness to $\ell_p$ norm-bounded
perturbations. We identify the sufficient conditions under which incorporating
additional generated data can improve robustness, and demonstrate that it is
possible to significantly reduce the robust-accuracy gap to models trained with
additional real data. Surprisingly, we even show that even the addition of
non-realistic random data (generated by Gaussian sampling) can improve
robustness. We evaluate our approach on CIFAR-10, CIFAR-100, SVHN and
TinyImageNet against $\ell_\infty$ and $\ell_2$ norm-bounded perturbations of
size $\epsilon = 8/255$ and $\epsilon = 128/255$, respectively. We show large
absolute improvements in robust accuracy compared to previous state-of-the-art
methods. Against $\ell_\infty$ norm-bounded perturbations of size $\epsilon =
8/255$, our models achieve 66.10% and 33.49% robust accuracy on CIFAR-10 and
CIFAR-100, respectively (improving upon the state-of-the-art by +8.96% and
+3.29%). Against $\ell_2$ norm-bounded perturbations of size $\epsilon =
128/255$, our model achieves 78.31% on CIFAR-10 (+3.81%). These results beat
most prior works that use external data.

本文探讨了如何使用仅基于原始训练集的生成模型来人为地增加原始训练集的大小并提高对扰动的鲁棒性，并证明了即使添加非现实的随机数据也可以提高鲁棒性。在 CIFAR-10、CIFAR-100、SVHN 和 TinyImageNet 上进行了大量的绝对准确度改进，对于常见扰动的数据集，本文方法的结果优于大多数使用外部数据的先前工作。

利用生成的数据提高鲁棒性

Improving Robustness using Generated Data

This paper describes the MeMAD project entry to the WMT Multimodal Machine
Translation Shared Task.
We propose adapting the Transformer neural machine translation (NMT)
architecture to a multi-modal setting. In this paper, we also describe the
preliminary experiments with text-only translation systems leading us up to
this choice.
We have the top scoring system for both English-to-German and
English-to-French, according to the automatic metrics for flickr18.
Our experiments show that the effect of the visual features in our system is
small. Our largest gains come from the quality of the underlying text-only NMT
system. We find that appropriate use of additional data is effective.

本文介绍了 MeMAD 项目进入 WMT 多模式机器翻译共享任务的情况，其中我们提出将 Transformer 神经机器翻译架构适应于多模式环境，并描述了我们在此选择之前对纯文本翻译系统的初步实验。我们拥有英语 - 德语和英语 - 法语两种语言的最高分数系统，在 flickr18 的自动指标中都得到了很好的表现。我们的实验表明，图像特征在我们的系统中作用不大，最大的收益来自于基于纯文本 NMT 系统的质量。我们发现适当地使用其他数据是有效的。