RI: Small: Collaborative Research: Active and Rapid Domain Generalization

机器学习的最新进展使得包括主动认证、自动驾驶和医疗诊断在内的广泛的实际应用成为可能。虽然机器学习算法在这些应用中取得了令人印象深刻的性能，但它们必须不断地处理输入数据不断变化的特征。这种情况的例子包括：在光照条件较差和侧面姿势的情况下识别人脸，同时在正面姿势的光照较好的人脸上训练算法；以及当现有算法针对高分辨率医学图像进行优化时，从低分辨率医学图像中检测和分割感兴趣的器官。这个问题通常被称为域移位。当域转移存在时，机器学习系统的精度显著降低。因此，用户必须花费大量的时间和金钱来重建机器学习模型，才能很好地处理新数据。该项目旨在开发自动检测域移动的存在的计算方法，快速使机器学习系统适应新的数据分布，并智能地寻找更多信息以提高系统的性能。该项目的研究成果，如软件、出版物和最佳做法，将有助于使各种机器学习系统不那么容易受到输入数据永久变化的影响，并在存在域转移的情况下更安全地使用。为了实现这些目标，本项目提出了四个主要任务：1)构建元学习技术，使分类器能够使用未标记数据有效地适应未知领域；2)开发用于查询附加信息的最佳强化学习策略，从而在不确定性较高的情况下允许有效的泛化；3)建立算法基础，用于检测领域移位的存在，并为适当的操作准备机器学习系统；以及4)通过与自主驾驶和主动移动认证应用相对应的大规模数据集的分类和分割任务来验证所提出的方法。这个项目结合了变分推理、强化学习和主动学习方面的最新进展，为如何处理领域转移问题带来了一个现代而独特的视角。这个奖项反映了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。

Recent advances in machine learning have enabled a wide range of practical applications including active authentication, autonomous driving, and medical diagnosis. While machine learning algorithms achieve impressive performances for these applications, they have to constantly deal with changing characteristics of input data. Examples of such cases include: recognizing faces under poor lighting conditions and side poses while algorithms are trained on well-illuminated faces at the frontal pose; and detecting and segmenting an organ of interest from low-resolution medical images when available algorithms are instead optimized for high-resolution medical images. This problem is commonly known as domain shift. The accuracies of machine learning systems decrease significantly when domain shifts are present. As a result, users must spend significant amounts of time and money to rebuild machine learning models to work well on new data. This project aims to develop computational methods for automatically detecting the presence of domain shifts, quickly adapting machine learning systems to new data distribution, and intelligently seeking additional information to improve the system's performance. Research outputs of this project, such as software, publications, and best practices will contribute to making a wide range of machine learning systems less vulnerable to perpetual changes of input data, and safer to use in the presence of domain shifts. To achieve these goals, this project proposes four main thrusts: 1) constructing meta-learning techniques to enable efficient adaptation of classifiers to unseen domains using unlabeled data; 2) developing an optimal reinforcement learning strategy for querying additional information that allows effective generalization when the uncertainty is high; 3) building algorithmic foundations for detecting the presence of domain shifts and preparing machine learning systems for appropriate actions; and 4) validating the proposed approaches with classification and segmentation tasks from large-scale datasets corresponding to autonomous driving and active mobile authentication applications. This project combines recent advances in variational inference, reinforcement learning, and active learning to bring a modern and unique perspective on how to deal with the problem of domain shift.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.