Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled
source domain and an unsupervised loss in an unlabeled target domain, which
often faces more severe overfitting (than classical supervised learning) as the
supervised source loss has clear domain gap and the unsupervised target loss is
often noisy due to the lack of annotations. This paper presents RDA, a robust
domain adaptation technique that introduces adversarial attacking to mitigate
overfitting in UDA. We achieve robust domain adaptation by a novel Fourier
adversarial attacking (FAA) method that allows large magnitude of perturbation
noises but has minimal modification of image semantics, the former is critical
to the effectiveness of its generated adversarial samples due to the existence
of 'domain gaps'. Specifically, FAA decomposes images into multiple frequency
components (FCs) and generates adversarial samples by just perturbating certain
FCs that capture little semantic information. With FAA-generated samples, the
training can continue the 'random walk' and drift into an area with a flat loss
landscape, leading to more robust domain adaptation. Extensive experiments over
multiple domain adaptation tasks show that RDA can work with different computer
vision tasks with superior performance.

本文提出了一种基于 Fourier 对抗攻击的鲁棒域自适应技术 (RDA)，可用于解决多个计算机视觉任务中的域自适应问题，其生成的对抗样本可以在使图像特征保持不变的情况下，减小由于域偏移带来的噪声问题，从而使模型的鲁棒性增强。

基于傅里叶对抗攻击的鲁棒领域自适应

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

Adversarial attacking aims to fool deep neural networks with adversarial
examples. In the field of natural language processing, various textual
adversarial attack models have been proposed, varying in the accessibility to
the victim model. Among them, the attack models that only require the output of
the victim model are more fit for real-world situations of adversarial
attacking. However, to achieve high attack performance, these models usually
need to query the victim model too many times, which is neither efficient nor
viable in practice. To tackle this problem, we propose a reinforcement learning
based attack model, which can learn from attack history and launch attacks more
efficiently. In experiments, we evaluate our model by attacking several
state-of-the-art models on the benchmark datasets of multiple tasks including
sentiment analysis, text classification and natural language inference.
Experimental results demonstrate that our model consistently achieves both
better attack performance and higher efficiency than recently proposed baseline
methods. We also find our attack model can bring more robustness improvement to
the victim model by adversarial training. All the code and data of this paper
will be made public.

该研究旨在阐述在自然语言处理领域，利用强化学习的攻击模型可以从历史攻击中学习，更高效地发起攻击，并对多项任务进行对比，实验证明其在攻击性和效率方面均优于其他基线方法。

学习攻击：朝着在现实场景中的文本对抗攻击

Learning to Attack: Towards Textual Adversarial Attacking in Real-world  Situations

While deep learning models and techniques have achieved great empirical
success, our understanding of the source of success in many aspects remains
very limited. In an attempt to bridge the gap, we investigate the decision
boundary of a production deep learning architecture with weak assumptions on
both the training data and the model. We demonstrate, both theoretically and
empirically, that the last weight layer of a neural network converges to a
linear SVM trained on the output of the last hidden layer, for both the binary
case and the multi-class case with the commonly used cross-entropy loss.
Furthermore, we show empirically that training a neural network as a whole,
instead of only fine-tuning the last weight layer, may result in better bias
constant for the last weight layer, which is important for generalization. In
addition to facilitating the understanding of deep learning, our result can be
helpful for solving a broad range of practical problems of deep learning, such
as catastrophic forgetting and adversarial attacking. The experiment codes are
available at this https URL

通过实验证明，神经网络的最后一个权重层收敛于最后一个隐藏层的线性 SVM，整个神经网络的训练会获得更好的偏移常量，从而有助于理解深度学习并解决实际问题。