论文标题
深度卷积烛台学习者的对抗性鲁棒性
Adversarial Robustness of Deep Convolutional Candlestick Learner
论文作者
论文摘要
深度学习（DL）已在广泛的领域中广泛应用。但是，已经表明，DL模型易受某种称为\ emph {对抗攻击}的扰动的影响。为了完全释放DL在金融交易等关键领域的功能，有必要解决此类问题。在本文中，我们提出了一种构建扰动示例的方法，并使用这些示例来增强模型的鲁棒性。我们的算法提高了DL模型在输入数据中的扰动方面的烛台分类的稳定性。
Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called \emph{adversarial attacks}. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of constructing perturbed examples and use these examples to boost the robustness of the model. Our algorithm increases the stability of DL models for candlestick classification with respect to perturbations in the input data.
