III: Small: Transfer Learning using Transformation among Models and Samples

III：小：利用模型和样本之间的转换进行迁移学习

• 批准号: 1813935
• 负责人: Chun-Sing Lee
• 金额: $ 49.99万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813935&HistoricalAwards=false
• 关键词: III; Small; Transfer; Learning; using

As huge volumes of unlabeled data are generated and made available in many domains, annotating data in these domains becomes burdensome and creates a major bottleneck in maintaining machine-learning databases. This project will investigate a family of transfer-learning methods as an automatic annotation tool, without human involvement, in annotating data for various machine-learning settings. The novelty of the project's transfer-learning approach is based on the common concept of matching-based optimization technique to solve the different forms of transfer learning. The optimization will be carried out using transformations at different levels for different forms. The planned transfer-learning framework will exploit lots of unlabeled data or a few labelled data in the target domain and prior knowledge in the form of labelled source data, source models or other auxiliary information in the source domain. Using this common matching-based optimization framework, this will bring out a natural transition from low-level, sample-based matching to high-level, model-based matching for the different forms of transfer learning. The family of transfer learning methods will have promising ramifications in diverse areas such as intelligent robots and self-driving cars so that they operate efficiently in new and changing environments without the need of large amount of annotated data in the new environments.This project will investigate two major forms of transfer learning -- domain adaptation and few-shot learning. The research will focus on studying the effect of the proposed matching-based optimization technique to solve the different forms of transfer learning. The project will focus on three major tasks, depending on what information is available in each task: (Task 1) Unsupervised domain adaptation, where the source-domain data is labelled while the target-domain data is unlabeled. In this case, the project team will investigate the optimization based on matching each source-domain sample with each target-domain sample to learn a generalizable target model; (Task 2) Hypothesis transfer learning, where the source and the target domain tasks are different, and only source models and sparsely labelled target domain data will be used to learn a generalizable target model. The model will be learned using matching between source models and target-domain samples; (Task 3) Few-shot learning, where the goal is to learn a generalizable target model from a few labelled samples in the target domain by utilizing auxiliary source knowledge. The project team will study whether transformation between source-model parameters can be substituted as useful auxiliary source-domain knowledge. Hence, the planned research will minimize the requirement of obtaining lots of labelled samples used in machine learning, and it will realize robust learning systems that are generalizable across tasks and domains. Furthermore, since the matching is carried out among each individual sample/model of information locally and explicitly, the results are expected to be better than previous methods.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.

随着大量未标记数据的生成和在许多领域中可用，在这些领域中注释数据变得繁重，并在维护机器学习数据库方面造成了主要瓶颈。 该项目将研究一系列迁移学习方法作为自动注释工具，无需人工参与，为各种机器学习设置注释数据。 该项目的迁移学习方法的新奇是基于匹配优化技术的共同概念，以解决不同形式的迁移学习。 优化将使用不同形式的不同级别的转换进行。 计划中的迁移学习框架将利用目标域中的大量未标记数据或少量标记数据以及源域中标记源数据、源模型或其他辅助信息形式的先验知识。 使用这种常见的基于匹配的优化框架，这将为不同形式的迁移学习带来从低级的基于样本的匹配到高级的基于模型的匹配的自然过渡。 迁移学习方法家族将在智能机器人和自动驾驶汽车等不同领域产生有前途的影响，使它们在新的和不断变化的环境中有效地运行，而不需要在新环境中使用大量的注释数据。本项目将研究迁移学习的两种主要形式--领域适应和少量学习。 本研究将重点研究所提出的基于匹配的优化技术解决不同形式的迁移学习的效果。 该项目将侧重于三个主要任务，这取决于每个任务中可用的信息：（任务1）无监督域适应，其中源域数据被标记，而目标域数据未被标记。 在这种情况下，项目团队将研究基于将每个源域样本与每个目标域样本匹配以学习可概括的目标模型的优化;（任务2）假设迁移学习，其中源和目标域任务不同，并且仅源模型和稀疏标记的目标域数据将用于学习可概括的目标模型。 模型将使用源模型和目标域样本之间的匹配来学习;（任务3）少镜头学习，其中目标是通过利用辅助源知识从目标域中的一些标记样本中学习可概括的目标模型。 项目组将研究源模型参数之间的转换是否可以作为有用的辅助源领域知识。 因此，计划中的研究将最大限度地减少获取机器学习中使用的大量标记样本的要求，并将实现跨任务和领域可推广的强大学习系统。 此外，由于匹配是在每个单独的样本/模型的信息进行本地和明确的，结果预计将优于以前的方法。这个奖项反映了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。

项目成果

Robot Behavior-Tree-Based Task Generation with Large Language Models

• DOI: 10.48550/arxiv.2302.12927
• 发表时间: 2023-02
• 期刊: ArXiv
• 作者: Yue Cao-;C. S. Lee

Behavior-Tree Embeddings for Robot Task-Level Knowledge

• DOI: 10.1109/iros47612.2022.9981774
• 发表时间: 2022-10
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Yue Cao;C.S. George Lee

Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration

• DOI: 10.1109/ijcnn.2019.8852315
• 发表时间: 2019-03
• 期刊: 2019 International Joint Conference on Neural Networks (IJCNN)
• 作者: Debasmit Das;C. S. G. Lee

Multi-Step Online Unsupervised Domain Adaptation

• DOI: 10.1109/icassp40776.2020.9052976
• 发表时间: 2020-02
• 期刊: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: J. Moon;Debasmit Das;George Lee

A Multistage Framework With Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation

• DOI: 10.1109/tip.2022.3186537
• 发表时间: 2022-06
• 期刊: IEEE Transactions on Image Processing
• 影响因子: 10.6
• 作者: Ji-Sun Moon;Debasmit Das;C. S. George Lee