Adversarial Training (AT) has become arguably the state-of-the-art algorithm
for extracting robust features. However, researchers recently notice that AT
suffers from severe robust overfitting problems, particularly after learning
rate (LR) decay. In this paper, we explain this phenomenon by viewing
adversarial training as a dynamic minimax game between the model trainer and
the attacker. Specifically, we analyze how LR decay breaks the balance between
the minimax game by empowering the trainer with a stronger memorization
ability, and show such imbalance induces robust overfitting as a result of
memorizing non-robust features. We validate this understanding with extensive
experiments, and provide a holistic view of robust overfitting from the
dynamics of both the two game players. This understanding further inspires us
to alleviate robust overfitting by rebalancing the two players by either
regularizing the trainer's capacity or improving the attack strength.
Experiments show that the proposed ReBalanced Adversarial Training (ReBAT) can
attain good robustness and does not suffer from robust overfitting even after
very long training. Code is available at this https URL

对抗训练中学习率衰减引起的非鲁棒特征记忆和非鲁棒过拟合问题进行了研究，并提出了重新平衡对抗训练方法以缓解这一问题。

平衡、不平衡与再平衡：从极小极大博弈角度理解鲁棒过拟合

Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from  a Minimax Game Perspective

Online reviews provide product evaluations for customers to make decisions.
Unfortunately, the evaluations can be manipulated using fake reviews ("spams")
by professional spammers, who have learned increasingly insidious and powerful
spamming strategies by adapting to the deployed detectors. Spamming strategies
are hard to capture, as they can be varying quickly along time, different
across spammers and target products, and more critically, remained unknown in
most cases. Furthermore, most existing detectors focus on detection accuracy,
which is not well-aligned with the goal of maintaining the trustworthiness of
product evaluations. To address the challenges, we formulate a minimax game
where the spammers and spam detectors compete with each other on their
practical goals that are not solely based on detection accuracy. Nash
equilibria of the game lead to stable detectors that are agnostic to any mixed
detection strategies. However, the game has no closed-form solution and is not
differentiable to admit the typical gradient-based algorithms. We turn the game
into two dependent Markov Decision Processes (MDPs) to allow efficient
stochastic optimization based on multi-armed bandit and policy gradient. We
experiment on three large review datasets using various state-of-the-art
spamming and detection strategies and show that the optimization algorithm can
reliably find an equilibrial detector that can robustly and effectively prevent
spammers with any mixed spamming strategies from attaining their practical
goal. Our code is available at this https URL

以极小化极大博弈为基础，利用依赖性马尔可夫决策过程（MDP）优化算法，定位鉴定在提高商品评价的真实性方面表现更优秀的网络欺诈检测与防范方法。

使用纳什强化学习实现强韧的垃圾用户检测

Robust Spammer Detection by Nash Reinforcement Learning

Given labeled instances on a source domain and unlabeled ones on a target
domain, unsupervised domain adaptation aims to learn a task classifier that can
well classify target instances. Recent advances rely on domain-adversarial
training of deep networks to learn domain-invariant features. However, due to
an issue of mode collapse induced by the separate design of task and domain
classifiers, these methods are limited in aligning the joint distributions of
feature and category across domains. To overcome it, we propose a novel
adversarial learning method termed Discriminative Adversarial Domain Adaptation
(DADA). Based on an integrated category and domain classifier, DADA has a novel
adversarial objective that encourages a mutually inhibitory relation between
category and domain predictions for any input instance. We show that under
practical conditions, it defines a minimax game that can promote the joint
distribution alignment. Except for the traditional closed set domain
adaptation, we also extend DADA for extremely challenging problem settings of
partial and open set domain adaptation. Experiments show the efficacy of our
proposed methods and we achieve the new state of the art for all the three
settings on benchmark datasets.

提出了一种基于判别式对抗领域适应 (DADA) 的新型对抗学习方法，其鼓励在输入实例的任何情况下，类别和域预测之间存在相互抑制关系，以解决现有方法中任务和域分类器相互独立导致的收敛问题，该方法能够在实际条件下定义一个最小极值博弈，促进联合分布对齐。除传统的闭集领域适应以外，还将 DADA 扩展到部分和开放集领域适应的极具挑战性的问题设置中，在基准数据集上实验表明了该方法的有效性并达到了三个场景下的最新成果。

判别式对抗领域自适应

Discriminative Adversarial Domain Adaptation

This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.

使用博弈理论的极小极大博弈，统一了生成式和判别式信息检索模型，并通过两者竞争优化了两个模型，其中生成式模型从判别式模型得到信号来学习文档相关性分布，而判别式模型通过生成式模型选择的无标签数据优化文档排名。