Collaborative Research: CIF: Medium: Taming Deep Unsupervised Representation Learning in Imaging: Theory and Algorithms

合作研究：CIF：媒介：驯服成像中的深度无监督表示学习：理论和算法

• 批准号: 2212066
• 负责人: Qing Qu
• 金额: $ 37.07万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212066&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; Taming

Deep neural networks are driving significant breakthroughs across engineering and scientific applications. Their success is often predicated on the availability of large and high-quality data sets. However, for many inverse problems or classification problems such as in computed tomography (CT), magnetic resonance imaging (MRI), and cryo-electron microscopy (cryo-EM), obtaining paired training data can be extremely difficult or expensive. The training data may also be limited or highly corrupted. While recent progress addresses these challenges via methods such as deep image prior or self-supervised learning, the training procedures therein are often ad hoc and the underlying mechanisms behind the approaches are far from well-understood, which are due to the lack of precise mathematical modeling and the lack of understanding of learned representations. To deal with these challenges, this project aims to develop robust unsupervised deep learning methods with rigorous guarantees in settings with limited and corrupted data by incorporating physical models and constraints that capture the intrinsic data structures and invariances effectively. The utility of the developed methods will be demonstrated on a variety of imaging applications, and the resulting findings and software will be widely disseminated. Furthermore, this project will develop a new educational program involving yearly virtual workshops with global participation and targeted K-12 outreach in southeast Michigan to enable improved participation in machine learning and computing programs, especially from underrepresented students.This project aims to develop robust, unsupervised deep representation learning methods with rigorous guarantees for application in inverse problems or classification problems in medical, industrial, and scientific imaging. The focus will be on learning deep models from limited and/or corrupted unpaired data. Deep representations will be designed and learned to capture the intrinsic structures and low-dimensionality of the data by leveraging ideas from traditional shallow learning methods (e.g., dictionary/transform learning). By understanding image characteristics captured during different stages of deep representation learning, physical models, priors, and constraints will be incorporated that enable deep networks to be learned efficiently from corrupted data. The deep representations will also be designed to be invariant to typical symmetry ambiguities such as translations and rotations. Based upon such principled modeling, efficient and robust optimization methods will be developed with theoretical guarantees for learning the intrinsic structures of the data, and the developed methods will be applied to a variety of imaging problems.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.

深度神经网络正在推动工程和科学应用的重大突破。它们的成功往往取决于能否获得大量高质量的数据集。然而，对于诸如计算机断层扫描（CT）、磁共振成像（MRI）和低温电子显微镜（cryo-EM）中的许多逆问题或分类问题，获得成对的训练数据可能极其困难或昂贵。训练数据也可能是有限的或高度损坏的。虽然最近的进展通过诸如深度图像先验或自监督学习等方法来解决这些挑战，但其中的训练过程通常是临时的，并且这些方法背后的潜在机制远未得到很好的理解，这是由于缺乏精确的数学建模和缺乏对学习表示的理解。为了应对这些挑战，该项目旨在开发强大的无监督深度学习方法，通过结合物理模型和约束，有效地捕获内在数据结构和不变性，在有限和损坏的数据环境中提供严格的保证。将在各种成像应用中展示所开发方法的效用，并将广泛传播由此产生的调查结果和软件。此外，该项目还将开发一个新的教育计划，包括每年举办一次全球参与的虚拟研讨会，并在密歇根州东南部有针对性地开展K-12推广活动，以提高机器学习和计算项目的参与度，特别是来自代表性不足的学生。该项目旨在开发鲁棒的，无监督的深度表示学习方法，并严格保证其在医学逆问题或分类问题中的应用，工业和科学成像。重点将是从有限和/或损坏的未配对数据中学习深度模型。深度表示将被设计和学习，以通过利用传统浅层学习方法（例如，字典/变换学习）。通过理解在深度表示学习的不同阶段捕获的图像特征，将结合物理模型、先验和约束，使深度网络能够从损坏的数据中有效地学习。深度表示也将被设计为对典型的对称性模糊性（如平移和旋转）不变。基于这样的原则性建模，将开发出高效和稳健的优化方法，为学习数据的内在结构提供理论保证，并将开发出的方法应用于各种成像问题。该奖项反映了NSF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。

项目成果

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold

• DOI: 10.48550/arxiv.2209.09211
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Can Yaras;Peng Wang;Zhihui Zhu;L. Balzano;Qing Qu

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning

• DOI: 10.48550/arxiv.2210.10041
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Shuo Xie;Jiahao Qiu;Ankita Pasad;Li Du;Qing Qu;Hongyuan Mei

Robust Training under Label Noise by Over-parameterization

• 发表时间: 2022-02
• 期刊: ArXiv
• 作者: Sheng Liu;Zhihui Zhu;Qing Qu;Chong You

On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features

• DOI: 10.48550/arxiv.2203.01238
• 发表时间: 2022-03
• 期刊: ArXiv
• 作者: Jinxin Zhou-;Xiao Li;Tian Ding;Chong You;Qing Qu;Zhihui Zhu

Are All Losses Created Equal: A Neural Collapse Perspective

• DOI: 10.48550/arxiv.2210.02192
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Jinxin Zhou-;Chong You;Xiao Li;Kangning Liu;Sheng Liu;Qing Qu;Zhihui Zhu