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

深度神经网络（DNN）的泛化已经成为一个具有挑战性的问题。当数据分布发生变化或输入中存在小干扰时，许多DNN无法保持预测性。这一挑战的一个主要原因是捷径学习，即根据现有数据中的关系做出决定，但这些关系不是因果关系。由于虚假的相关性，当模型被转移到现实世界的场景中时，这些决策就会失败。该项目旨在研究深度学习中的捷径识别和缓解。这项研究的成功结果将导致在提供理论理解方面取得进展，并开发强大且可推广的DNN算法来分析具有各种类型快捷方式的数据集。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，以吸引、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，以促进深度学习的推广。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。具体而言，该项目研究了不同级别的快捷方式识别和检测，包括实例，特征和任务级别，然后通过数据增强和训练正则化来执行快捷方式缓解。该项目还展示了如何将拟议的研究创新嵌入到两个基于DNN的真实的医疗信息系统中。建议的框架揭示了捷径学习的内在属性，通过校准不同类别的分布变化的捷径功能，并使他们的理解和采用的研究人员和practitioners.This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。

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.