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不能保持预测性。这种行为的一个常见原因是“捷径学习”，即DNN学习根据数据中观察到的关系做出决定，但这些关系不是因果关系。当模型被转移到真实世界的场景中时，这些决定会失败，因为网络已经锁定了虚假的关联。本项目研究如何识别和缓解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，以及避免捷径的健壮和可推广的DNN算法。该教育计划整合了机器学习、工业工程和健康信息学，以培训学生在信息系统中使用基本的数据分析工具，并从代表不足的群体中吸引、指导和留住成员。该项目的主要目标是从以数据为中心的角度系统地调查快捷特征的识别和缓解，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用于真实世界的数据分析系统，以缓解快捷学习的缺点。该项目从实例、特征、任务等不同层面研究路径识别和检测，并通过数据扩充和训练正则化进行路径缓解。该项目还演示了如何将拟议的研究创新嵌入到两个真正的基于DNN的医疗信息学系统中。拟议的框架通过在不同类型的分布变化中校准快捷方式特征来揭示快捷学习的内在属性，并应支持研究人员和实践者。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.