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

深度神经网络（DNN）泛化是一个具有挑战性的问题。许多DNN在数据分布发生变化或其输入存在小干扰时无法保持预测性。这种行为的一个常见原因是“捷径学习”，其中DNN学习根据数据中观察到的关系做出决策，但这些关系不是因果关系。当模型被转移到现实世界的场景中时，这些决策就会失败，因为网络已经锁定了虚假的相关性。该项目研究如何识别和减轻DNN中的捷径学习。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。该教育项目整合了机器学习、工业工程和健康信息学，旨在培养学生掌握信息系统中的基本数据分析工具，并吸引、指导和留住来自代表性不足群体的成员。该项目的主要目标是从以数据为中心的角度系统地研究识别和缓解捷径特征，以促进深度学习中的泛化。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，以减轻捷径学习的缺点。该项目研究了不同级别的快捷方式识别和检测，包括实例，功能和任务级别，然后通过数据增强和训练正则化来执行快捷方式缓解。该项目还展示了拟议的研究创新如何嵌入到两个真实的基于DNN的医疗信息系统中。建议的框架通过校准不同类型的分布变化的捷径特征，揭示了捷径学习的内在属性，并应支持研究人员和从业人员。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.