Despite the effectiveness in improving the robustness of neural networks,
adversarial training has suffered from the natural accuracy degradation
problem, i.e., accuracy on natural samples has reduced significantly. In this
study, we reveal that natural accuracy degradation is highly related to the
disruption of the natural sample topology in the representation space by
quantitative and qualitative experiments. Based on this observation, we propose
Topology-pReserving Adversarial traINing (TRAIN) to alleviate the problem by
preserving the topology structure of natural samples from a standard model
trained only on natural samples during adversarial training. As an additional
regularization, our method can easily be combined with various popular
adversarial training algorithms in a plug-and-play manner, taking advantage of
both sides. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet
show that our proposed method achieves consistent and significant improvements
over various strong baselines in most cases. Specifically, without additional
data, our proposed method achieves up to 8.78% improvement in natural accuracy
and 4.50% improvement in robust accuracy.

通过保留自然样本的拓扑结构，我们的研究提出了一种名为 Topology-pReserving Adversarial traINing (TRAIN) 的对抗训练方法，以缓解自然准确率降低的问题，并在多个测试数据集上实验证明在自然准确率和鲁棒准确率上都取得了显著的改善。

拓扑保持对抗训练

Topology-Preserving Adversarial Training

Image captioning has been recently gaining a lot of attention thanks to the
impressive achievements shown by deep captioning architectures, which combine
Convolutional Neural Networks to extract image representations, and Recurrent
Neural Networks to generate the corresponding captions. At the same time, a
significant research effort has been dedicated to the development of saliency
prediction models, which can predict human eye fixations. Even though saliency
information could be useful to condition an image captioning architecture, by
providing an indication of what is salient and what is not, research is still
struggling to incorporate these two techniques. In this work, we propose an
image captioning approach in which a generative recurrent neural network can
focus on different parts of the input image during the generation of the
caption, by exploiting the conditioning given by a saliency prediction model on
which parts of the image are salient and which are contextual. We show, through
extensive quantitative and qualitative experiments on large scale datasets,
that our model achieves superior performances with respect to captioning
baselines with and without saliency, and to different state of the art
approaches combining saliency and captioning.

本文提出了一种基于生成循环神经网络的图像字幕方法，它可以利用一个显著性预测模型在生成字幕时集中于图像不同的部分，并通过大规模数据集上的广泛定量和定性实验证明该模型相比不带显著性和不同的最先进的显著性与字幕结合方法具有卓越的性能。