Collaborative Research: Robust Deep Learning in Real Physical Space: Generalization, Scalability, and Credibility

深度神经网络对微小和难以察觉的扰动的脆弱性是当今机器学习的主要挑战。对于自动驾驶汽车、安全和医疗诊断等各种应用，这一弱点严重限制了机器学习系统的大规模部署。现有的理论研究虽然在先进的统计分析基础上奠定了良好的基础，但却需要各种理想的假设，难以在真实的物理环境中得到验证。因此，理解深度学习算法的鲁棒性及其与真实物理环境的相互作用是更好地理解可解释性、泛化和可信度的关键一步。该项目旨在通过开发可以实际验证的新理论和计算机视觉系统来缩小差距。研究成果将创造可转化为更安全可靠的商业产品的新技术，从而加强美国的全球竞争力；新的可信赖的人工智能系统，可用于监控和国防产品，以改善美国的国家安全；通过开发从K-12到本科研究、研究生指导、行业合作、在线学习模块和课程开发的完整培训管道，扩大下一代劳动力的能力；通过利用基础研究概念的可及性和吸引力，开展针对从小学到研究生的女性参与者的教育外展，扩大STEM的参与；促进统计学、理论计算机科学和图像处理领域的跨学科思想交流。真实物理空间中的鲁棒机器学习需要对深度神经网络和神经网络运行的环境进行共同建模。专注于一个特定领域而不与另一个领域相互作用的研究努力不可能解决问题。普渡大学-加州大学圣地亚哥分校的团队将电气工程、统计学和计算机科学的技能结合起来，为解决这一问题提供了一个独特的机会。团队将采用的技术方法是通过结合环境因素来重新制定健壮的对抗性学习问题。将追求四个具体的研究目标：(1)通过确定性和生成方法的层次结构来参数化物理环境，以便约束所有可能的扭曲集合。(2)分析了存在环境因素时神经网络的泛化边界，并通过研究鲁棒性和不确定性量化分析了该系统的可信度。(3)开发计算效率高的算法来寻求所提出的极大极小优化的平衡点。(4)建立计算摄影实验平台，实现概念并验证理论结果。在教育方面，该项目为K-12提供了一系列外展活动，以提高他们对STEM的兴趣，并为本科生提供了研究机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The vulnerability of deep neural networks to small and imperceptible perturbations is a major challenge in machine learning today. For a variety of applications such as autonomous vehicles, security, and medical diagnosis, this weakness has severely limited the deployment of machine learning systems at scale. Existing theoretical studies, while laying a good foundation based on advanced statistical analyses, require various idealistic assumptions that are difficult to be validated in real physical environments. Understanding the robustness of deep learning algorithms and its interactions with the real physical environment is therefore a critical step towards a better understanding of explainability, generalization, and trustworthiness. This project aims to close the gap by developing new theories and computer vision systems that can be realistically validated. The outcomes of the research will create new technologies that can be translated into more secure and reliable commercial products, hence strengthening the global competitiveness of the United States; new trustworthy AI systems that can be deployed for surveillance and defense products to improve the national security of the United States; expand the next-generation workforce capacity by developing a complete training pipeline from K-12 outreach to undergraduate research, graduate mentoring, industry partnership, online learning modules, and curriculum development; broaden participation in STEM by leveraging the accessibility and intrigue of the foundational research concepts to conduct educational outreach that targets female participants from elementary up through graduate school; and promote the exchanges of ideas across disciplines in statistics, theoretical computer science, and image processing. Robust machine learning in real physical space requires co-modeling the deep neural networks and the environment in which the neural networks are operating. Research efforts focusing on one specific domain but not interacting with the other domain will unlikely solve the problem. The combination of skills in electrical engineering, statistics, and computer science possessed by the Purdue-UCSD team offers a unique opportunity to address the problem. The technical approach the team will take is to reformulate the robust adversarial learning problem by incorporating the environmental factors. Four specific research objectives will be pursued: (1) Parametrizing the physical environment via a hierarchy of deterministic and generative approaches, so that the set of all possible distortions can be constrained. (2) Analyzing the generalization bounds of neural networks in the presence of the environmental factors and analyzing the credibility of such a system by studying the robustness and uncertainty quantification. (3) Developing computationally efficient algorithms to seek the equilibrium points of a proposed minimax optimization. (4) Building a computational photography testbed to implement the concepts and validate the theoretical results. On the educational front, the project provides a suite of outreach activities to K-12 to improve their interest in STEM, and research opportunities to undergraduates.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.