DeepView：使用判别性简化将深度神经网络的分类边界可视化为散点图
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction
摘要：
使用深度架构的机器学习算法已经能够实现越来越强大和成功的模型。然而，它们也变得越来越复杂，更难理解，更容易被愚弄。到目前为止，文献中的大多数方法都是针对单个给定的输入数据来研究模型的决策。在本文中，我们提出了可视化的一部分决策功能的深度神经网络与一部分的数据集在两个维度与歧视性降维。这使我们能够检查模型如何处理数据的不同属性，例如离群值，对手或中毒数据。此外，所提出的方法是对文献中提到的解释方法的补充，因此与这些方法结合可能更有用。代码可在https://github.com/LucaHermes/DeepView上获得。
英文摘要：
Machine learning algorithms using deep architectures have been able to implement increasingly powerful and successful models. However, they also become increasingly more complex, more difficult to comprehend and easier to fool. So far, most methods in the literature investigate the decision of the model for a single given input datum. In this paper, we propose to visualize a part of the decision function of a deep neural network together with a part of the data set in two dimensions with discriminative dimensionality reduction. This enables us to inspect how different properties of the data are treated by the model, such as outliers, adversaries or poisoned data. Further, the presented approach is complementary to the mentioned interpretation methods from the literature and hence might be even more useful in combination with those. Code is available at https://github.com/LucaHermes/DeepView .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
