Although adversarial training (AT) has proven effective in enhancing the
model's robustness, the recently revealed issue of fairness in robustness has
not been well addressed, i.e. the robust accuracy varies significantly among
different categories. In this paper, instead of uniformly evaluating the
model's average class performance, we delve into the issue of robust fairness,
by considering the worst-case distribution across various classes. We propose a
novel learning paradigm, named Fairness-Aware Adversarial Learning (FAAL). As a
generalization of conventional AT, we re-define the problem of adversarial
training as a min-max-max framework, to ensure both robustness and fairness of
the trained model. Specifically, by taking advantage of distributional robust
optimization, our method aims to find the worst distribution among different
categories, and the solution is guaranteed to obtain the upper bound
performance with high probability. In particular, FAAL can fine-tune an unfair
robust model to be fair within only two epochs, without compromising the
overall clean and robust accuracies. Extensive experiments on various image
datasets validate the superior performance and efficiency of the proposed FAAL
compared to other state-of-the-art methods.

在这篇论文中，我们提出了一种名为公平感知对抗学习（FAAL）的新的学习范式，通过将鲁棒训练问题重新定义为最小 - 最大 - 最大框架，以确保训练模型的鲁棒性和公平性。具体而言，通过利用分布鲁棒优化，我们的方法旨在在不同类别之间找到最差的分布，解决方法保证了高概率的上界性能。FAAL 可以在仅两个迭代周期内将一个不公平的鲁棒模型调整为公平模型，而不牺牲整体准确性和鲁棒性，实验证明了 FAAL 相对于其他先进方法的卓越性能和高效性。

走向公平感知的对抗学习

Towards Fairness-Aware Adversarial Learning

Recent deep models for solving routing problems always assume a single
distribution of nodes for training, which severely impairs their
cross-distribution generalization ability. In this paper, we exploit group
distributionally robust optimization (group DRO) to tackle this issue, where we
jointly optimize the weights for different groups of distributions and the
parameters for the deep model in an interleaved manner during training. We also
design a module based on convolutional neural network, which allows the deep
model to learn more informative latent pattern among the nodes. We evaluate the
proposed approach on two types of well-known deep models including GCN and
POMO. The experimental results on the randomly synthesized instances and the
ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our
approach could significantly improve the cross-distribution generalization
performance over the original models.

本文研究了应用于解决路由问题的深度模型在训练时通常只考虑单个数据分布，导致它们的交叉分布泛化能力受到严重削弱，因此提出采用分组分布式鲁棒优化来解决该问题，并设计了基于卷积神经网络的模块，以便让深度模型学习更多节点之间的相关信息。实验结果表明，该方法能显著提高原始模型的交叉分布泛化性能。