Collaborative Research: III: Medium: Towards Effective Detection and Mitigation for Shortcut Learning: A Data Modeling Framework

协作研究：III：媒介：针对捷径学习的有效检测和缓解：数据建模框架

• 批准号: 2310260
• 负责人: Xia Hu
• 金额: $ 60万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310260&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; Towards

Deep Neural Network (DNN) generalization is a challenging problem. Many DNNs do not remain predictive when the distribution of data changes or there are small disturbances to their input. A common reason for this behavior is “shortcut learning”, in which the DNN learns to make decisions based on relationships observed in the data, but that are not causal. These decisions fail when the model is transferred to real-world scenarios because the network has latched onto spurious correlations. This project investigates how to identify and mitigate shortcut learning in DNNs. A successful outcome of this research will lead to advances in theoretical understanding, as well as robust and generalizable DNN algorithms that avoid shortcuts. The education program integrates machine learning, industrial engineering, and health informatics to train students with essential data analytics tools in information systems, as well as to attract, mentor and retain members from underrepresented groups.The primary goal of this project is to systematically investigate the identification and mitigation of shortcut features from a data-centric perspective to facilitate generalization in deep learning. The developed data-centric mechanisms could be directly adopted in real-world data analytics systems to mitigate the drawbacks of shortcut learning. The project studies shortcut identification and detection at different levels, including instance, feature, and task levels, and then performs shortcut mitigation through data augmentation and training regularization. The project also demonstrates how the proposed research innovations could be embedded into two real DNN-based medical informatics systems. The proposed framework uncovers intrinsic properties of shortcut learning by calibrating shortcut features across different types of distribution shifts, and should support both researchers and practitioners.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

深神经网络（DNN）概括是一个挑战问题。当数据变化的分布或输入的小灾难时，许多DNN不会保持预测。这种行为的一个普遍原因是“快捷学习”，其中DNN学会根据数据中观察到的关系做出决定，但不是因果关系。当该模型转移到现实世界情景时，这些决定失败了，因为网络已锁定在虚假相关性上。该项目研究了如何识别和减轻DNN中的快捷方式学习。这项研究的成功结果将导致理论理解的进步，以及避免捷径的鲁棒和可推广的DNN算法。该教育计划将机器学习，工业工程和健康信息纳入了信息系统中的基本数据分析工具，并吸引，精神和保留成员来自代表性不足的群体的成员。该项目的主要目标是系统地研究和减轻捷径识别和减轻捷径特征，从数据量表的角度来促进一般学习的一般学习。可以在现实世界数据分析系统中直接采用开发的以数据为中心的机制来减轻快捷方式学习的缺点。该项目研究在不同级别的捷径识别和检测，包括实例，功能和任务级别，然后通过受到数据增强和培训的调节来进行快捷措施缓解。该项目还展示了如何将拟议的研究创新嵌入两个基于DNN的实际医学信息系统中。拟议的框架通过校准不同类型的分配转移的快捷方式来揭示快捷方式学习的内在属性，并应支持研究人员和实践者。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。