CAREER: Enabling Continual Multi-view Representation Learning: An Adversarial Perspective

表征学习技术试图从原始数据中提取和提取关键信息(即特征)，用于广泛应用的分析，如网络安全、工业、金融、经济和科学发现。表示学习是机器学习系统中的一个关键步骤，其目的是使表示学习具有较强的鲁棒性，而不受噪声引起的原始数据突变或捕获设备引起的原始数据变化的影响。在大数据时代，表征学习技术面临着新的挑战。从不同传感器(例如，多视角摄像机系统)收集的海量数据或以不同形式(例如，视听-文本)呈现的海量数据已经使现有的表征学习技术超载。此外，从互联网收到的流动数据和随着时间积累的敏感数据，如个人相册和电子健康记录，需要已建立的表征学习模型来适应和考虑传入的数据。这个项目将开发一个稳健的持续表征学习模型来应对这些挑战。在数据访问受限(如敏感数据)或设备的处理能力有限(如EDGE和移动设备)的现实世界场景中，利益相关者将受益于自适应表示学习技术，以实现连续的数据分析。该项目旨在通过将机器智能和人类知识集成在支持人工智能的安全环境中，促进对连续多视图稳健表示学习的基本理解。有三个独特的贡献。首先，该项目将研究多视图一致性追求，以融合知识并生成对现实世界数据中频繁遇到的域移动具有健壮性的视图不变表示。其次，本研究将重新审视和探索多视点情境下的对抗性学习，以实现新的攻击模式，包括迭代、交叉视点和诱导模式。生成的对抗性样本和训练过程将使所获得的多视图表示学习模型受益并增强其能力，以减少各种形式的人工噪声。第三，将通过一种新的内存受限搜索树来创建新的持续学习模型，以支持在连续数据流的情况下多视图表示学习的进化。此外，为了减少搜索空间和与数据相关的不确定性，这项研究将利用人类知识为拟议的持续学习模型获取关键注释和经验策略。这一奖项反映了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。

Representation learning techniques attempt to extract and abstract key information (i.e., the features) from raw data to be used in analyses in a wide range of applications, such as cybersecurity, industry, finance, economics, and scientific discovery. As a critical step in machine learning systems, representation learning is meant to be robust in its capacity, regardless of the mutation of raw data due to noises or the variations of raw data caused by capture devices. In the era of big data, representation learning techniques are confronted with new challenges. Massive data collected from different sensors (e.g., the multi-view camera system) or presented in different modalities (e.g., audio-visual-text) have overloaded existing representation learning techniques. In addition, streaming data received from the Internet and sensitive data accumulated over time, such as personal albums and electronic health records, require the established representation learning model to adapt and account for incoming data. This project will develop a robust continual representation learning model to address these challenges. In real-world scenarios where data access is restricted (e.g., sensitive data) or the processing power of devices is limited (e.g., edge and mobile devices), stakeholders will benefit from the adaptive representation learning techniques to enable continual data analyses.This project seeks to advance the fundamental understanding of continual multi-view robust representation learning by integrating machine intelligence and human knowledge in AI-enabled security contexts. There are three unique contributions. First, the project will investigate multi-view consistency pursuit to fuse knowledge and generate a view-invariant representation robust to domain shifts frequently encountered in real-world data. Second, this research will revisit and explore adversarial learning in multi-view contexts to enable new attack modes, including iterative, cross-view, and induced modes. Generated adversarial samples and training procedures will benefit and empower the acquired multi-view representation learning models to mitigate various forms of artificial noise. Third, new continual learning models will be created through a novel Memory Bounded Search Tree to enable the evolution of multi-view representation learning despite continual streams of data. Furthermore, to reduce the search space and uncertainty related to the data, this research will leverage human knowledge to acquire critical annotations and empirical strategies for the proposed continual learning models.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.