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: 2310262
• 负责人: Na Zou
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 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=2310262&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; Towards

Generalization of Deep Neural Networks (DNNs) has become a challenging problem. Many DNNs do not remain predictive when the distribution of data changes or there are small disturbances in the input. A major reason for this challenge is shortcut learning, which refer to decisions based on relationships in the data that exist, but which are not causal. These decisions fail when the model is transferred to real-world scenarios because of spurious correlations. This project is to investigate shortcut identification and mitigation in deep learning. The successful outcome of this research will lead to advances in providing theoretical understandings, and developing robust and generalizable DNN algorithms to analyze datasets with various types of shortcuts. The education program that integrates machine learning, industrial engineering, and health informatics is to train students with essential data analytics tools in information systems, 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 the generalization of deep learning. The developed data-centric mechanisms could be directly adopted in real-world data analytics systems. Specifically, this 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. This project also demonstrates how the proposed research innovations could be embedded in two DNN based real medical informatics systems. The proposed frameworks uncover the intrinsic properties of shortcut learning by calibrating shortcut features from different categories of distribution shift, and enable their comprehension and adoption for 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.

深度神经网络（DNN）的泛化已成为一个具有挑战性的问题。当数据分布发生变化或输入存在微小干扰时，许多 DNN 就无法保持预测性。这一挑战的一个主要原因是捷径学习，它是指基于数据中存在的关系做出的决策，但这些关系不是因果关系。当模型转移到现实场景时，由于虚假相关性，这些决策会失败。该项目旨在研究深度学习中的捷径识别和缓解。这项研究的成功成果将在提供理论理解和开发强大且可推广的 DNN 算法来分析具有各种类型的快捷方式的数据集方面取得进展。该教育项目融合了机器学习、工业工程和健康信息学，旨在培训学生掌握信息系统中必要的数据分析工具，以吸引、指导和留住弱势群体的成员。该项目的主要目标是以数据为中心的角度系统地研究快捷特征的识别和缓解，以促进深度学习的泛化。开发的以数据为中心的机制可以直接应用于现实世界的数据分析系统。具体来说，该项目研究不同级别（包括实例级别、特征级别和任务级别）的快捷方式识别和检测，然后通过数据增强和训练正则化来执行快捷方式缓解。该项目还展示了如何将所提出的研究创新嵌入到两个基于 DNN 的真实医学信息系统中。所提出的框架通过校准不同类别的分布变化的快捷特征来揭示快捷学习的内在属性，并使研究人员和从业者能够理解和采用它们。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。