来自重力波检测器的数据中出现的瞬态噪声通常会引起问题，例如检测器的不稳定性以及重叠或模仿重力波信号。由于瞬态噪声被认为与环境和工具相关联，因此其分类将有助于理解其起源并改善探测器的性能。在先前的研究中，提出了用于使用时频2D图像（频谱图）进行瞬态噪声进行分类的体系结构，该架构使用了无监督的深度学习与变异自动编码器和不变信息集群的结合。提出的无监督学习结构应用于重力间谍数据集，该数据集由高级激光干涉仪重力波动台（Advanced Ligo）瞬态噪声与其相关元数据进行讨论，以讨论在线或离线数据分析的潜力。在这项研究的重点是重力间谍数据集中，研究并报告了先前研究的无监督学习结构的训练过程。
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Adversarial training is a method for enhancing neural networks to improve the robustness against adversarial examples. Besides the security concerns of potential adversarial examples, adversarial training can also improve the generalization ability of neural networks, train robust neural networks, and provide interpretability for neural networks. In this work, we introduce adversarial training in time series analysis to enhance the neural networks for better generalization ability by taking the finance field as an example. Rethinking existing research on adversarial training, we propose the adaptively scaled adversarial training (ASAT) in time series analysis, by rescaling data at different time slots with adaptive scales. Experimental results show that the proposed ASAT can improve both the generalization ability and the adversarial robustness of neural networks compared to the baselines. Compared to the traditional adversarial training algorithm, ASAT can achieve better generalization ability and similar adversarial robustness.
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