论文标题
信任在哪里分解？通过信任矩阵和有条件信任密度对深神经网络的定量信任分析
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities
论文作者
论文摘要
近年来，深度学习的进步和成功导致了对广泛无处不在的广泛采用的努力和投资，从个人助理和智能导航到电子商务中的搜索和产品推荐。随着深度学习采用的巨大兴起，有关为这些应用程序提供动力的深神经网络的可信度问题。有动力回答此类问题，最近对信任量化引起了人们的兴趣。在这项工作中，我们介绍了Trust Matrix的概念，这是一种新颖的信任量化策略，它利用了Wong等人最近引入的问答信任度量指标。为了提供更深入的，更详细的见解，以了解给定深层神经网络的信任分解的位置。更具体地说，信任矩阵定义了给定的演员轨道答案方案的预期问题答案信任，从而使人们可以快速发现需要解决的低信任区域，以提高深度神经网络的可信度。拟议的信任矩阵很容易计算，可解释的，并且据作者所知，这是第一个在Actor-Oracle答案级别上研究信任的人。我们通过有条件的信任密度概念进一步扩展了信任密度的概念。我们通过实验性地利用信任矩阵来研究几种众所周知的深神网络体系结构，以识别图像，并进一步研究了一个有趣的演员 - 奥科尔（Oracle-Oracle）答案方案的信任密度和条件信任密度。结果表明，信任矩阵以及有条件的信任密度，除了现有的信任量化指标套件外，还可以是指导从业人员和监管机构在创建和认证深度学习解决方案的可信赖操作方面。
The advances and successes in deep learning in recent years have led to considerable efforts and investments into its widespread ubiquitous adoption for a wide variety of applications, ranging from personal assistants and intelligent navigation to search and product recommendation in e-commerce. With this tremendous rise in deep learning adoption comes questions about the trustworthiness of the deep neural networks that power these applications. Motivated to answer such questions, there has been a very recent interest in trust quantification. In this work, we introduce the concept of trust matrix, a novel trust quantification strategy that leverages the recently introduced question-answer trust metric by Wong et al. to provide deeper, more detailed insights into where trust breaks down for a given deep neural network given a set of questions. More specifically, a trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario, allowing one to quickly spot areas of low trust that needs to be addressed to improve the trustworthiness of a deep neural network. The proposed trust matrix is simple to calculate, humanly interpretable, and to the best of the authors' knowledge is the first to study trust at the actor-oracle answer level. We further extend the concept of trust densities with the notion of conditional trust densities. We experimentally leverage trust matrices to study several well-known deep neural network architectures for image recognition, and further study the trust density and conditional trust densities for an interesting actor-oracle answer scenario. The results illustrate that trust matrices, along with conditional trust densities, can be useful tools in addition to the existing suite of trust quantification metrics for guiding practitioners and regulators in creating and certifying deep learning solutions for trusted operation.
