III: Small: Collaborative Research: Effective Labeled Data Generation via Generative Adversarial Learning

最近在应用深度学习解决许多具有挑战性的数据科学问题方面取得的成功，部分原因是大规模标记训练数据的可用性。然而，创建大规模的标记数据集是耗时的，劳动密集型的，昂贵的，并且通常需要大量的领域知识。因此，许多现实世界的应用程序只提供具有有限标签信息的数据（即，少量标记数据或没有标记数据）。因此，缺乏标记训练数据仍然是将深度学习技术应用于具有挑战性的数据科学问题的主要障碍之一。另一方面，生成对抗学习的最新进展在生成真实数据方面显示出有希望的结果，这可以为缓解缺乏标记训练数据的问题提供新的视角。因此，本项目通过生成对抗学习探索有效的标记数据生成。提出的研究将最先进的标记数据生成和生成对抗学习扩展到一个新的前沿，调查了需要创新解决方案的原始问题，并为有效驯服合成标记数据生成的新研究努力铺平了道路。由于许多现实世界的问题都面临着有限标记数据的挑战，因此该项目有可能使来自计算机科学、教育、政治、医疗保健和生物信息学等各个学科的许多实际应用受益。本项目提出了基于生成对抗学习的新方法，用于有效的标签数据生成，以促进有限标签信息的深度学习，研究相关的基础研究问题并开发有效的算法。它有三个主要的研究目标。首先，当可用的标记数据较少时，它探索从未标记的数据中估计底层数据分布，并将标签信息合并用于标记数据生成，包括极度不平衡的数据和不完整的标签场景。其次，当没有标记数据时，它采用另一种弱监督（例如，不准确的标签，不精确的标签和成对约束）来生成标记数据。第三，当既没有标记数据也没有弱监督时，它探索将人类参与整合到生成对抗学习中以提供监督。计划采用不同的方式来传播项目及其发现，例如网络支持的数据和软件存储库、书籍、期刊和会议出版物、特殊目的讲习班或教程以及工业合作。该项目可以有效地整合到本科和研究生课程以及学生的研究项目中。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。

Recent successes in applying deep learning to solve many challenging data science problems is in part due to the availability of large-scale labeled training data. However, creating large-scale labeled datasets is time consuming, labor-intensive, costly, and often requires significant domain knowledge. Many real-world applications, therefore, come with only data with limited label information (i.e., a small amount of labeled data or no labeled data). Thus, lack of labeled training data is still one of major roadblocks in applying deep learning techniques to challenging data science problems. On the other hand, recent advancements in generative adversarial learning have shown promising results in generating realistic data, which could enable a new perspective for alleviating the problem of lacking labeled training data. Thus, this project explores effective labeled data generation via generative adversarial learning. The proposed research extends the state-of-the-art labeled data generation and generative adversarial learning to a new frontier, investigates original problems that entreat innovative solutions and paves the way for a new research endeavor effectively tame synthetic labeled data generation. As many real-world problems face the challenge of limited labeled data, the project has potential to benefit many real-world applications from various disciplines such as Computer Science, Education, Politics, Healthcare and Bioinformatics.This project proposes novel approaches based on generative adversarial learning for effective labeled data generation to facilitate deep learning with limited label information, investigates associated fundamental research issues and develops effective algorithms. It has three primary research objectives. First, when a small amount of labeled data is available, it explores to estimate the underlying data distribution from unlabeled data and incorporate the label information for labeled data generation, including extremely imbalanced data and incomplete label scenarios. Second, when labeled data is not available, it adopts an alternative weak supervision (e.g., inaccurate labels, inexact labels and pairwise constraints) for generating labeled data. Third, when neither labeled data nor weak supervision is available, it explores to integrate human involvement to generative adversarial learning for providing supervision. Disparate means are planned to disseminate the project and its findings, such as web enabled data and software repositories, books, journal and conference publications, special purpose workshops or tutorials, and industrial collaborations. The project can be effectively integrated to undergraduate and graduate courses as well as in student research projects.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.