Care Label概念：用于可信赖和资源感知机器学习的认证套件
The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning
摘要：
机器学习应用已经变得无处不在。这导致人们越来越努力地使机器学习值得信赖。可解释和公平的人工智能已经成熟。它们面向知识渊博的用户和应用工程师。对于那些不想花时间去理解方法或学习模型的人，我们提供了护理标签：一目了然，允许方法或模型比较，同时，有科学依据。一方面，这改变了由例如，概况介绍或模型卡，转换成非常适合最终用户的形式。另一方面，护理标签是测试所述保证是否有效的认证套件的结果。在本文中，我们提出了两个实验，我们的认证套件。一个显示了马尔可夫随机场（MRF）的配置的护理标签。基于MRF的基本理论，每个选择都会导致其静态属性的特定评级，例如，表现力和可靠性。此外，实施测试和资源消耗的测量产生动态属性。这个两级过程之后是另一个验证深度神经网络（DNN）模型的实验。在那里，我们从文献中提取特定模型和数据集的静态属性。在第二个层次上，生成实验，提供对某些攻击的鲁棒性的测量。我们通过ResNet-18和MobileNetV 3应用于ImageNet来说明这一点。
英文摘要：
Machine learning applications have become ubiquitous. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They address knowledgeable users and application engineers. For those who do not want to invest time into understanding the method or the learned model, we offer care labels: easy to understand at a glance, allowing for method or model comparisons, and, at the same time, scientifically well-based. On one hand, this transforms descriptions as given by, e.g., Fact Sheets or Model Cards, into a form that is well-suited for end-users. On the other hand, care labels are the result of a certification suite that tests whether stated guarantees hold. In this paper, we present two experiments with our certification suite. One shows the care labels for configurations of Markov random fields (MRFs). Based on the underlying theory of MRFs, each choice leads to its specific rating of static properties like, e.g., expressivity and reliability. In addition, the implementation is tested and resource consumption is measured yielding dynamic properties. This two-level procedure is followed by another experiment certifying deep neural network (DNN) models. There, we draw the static properties from the literature on a particular model and data set. At the second level, experiments are generated that deliver measurements of robustness against certain attacks. We illustrate this by ResNet-18 and MobileNetV3 applied to ImageNet.
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。
