EAGER: Constraint Aware Generative Adversarial Networks

生成对抗网络通过对抗性博弈来估计真实的数据分布，在图像生成和文本生成方面取得了巨大的成功。然而，来自真实的世界应用的许多决策模型都是在包含数值和分类属性的关系数据上训练的。此外，真实的世界应用通常对所生成的数据具有法律的或道德的要求，例如，无歧视或隐私保护，或期望生成可能仅包含正常样本的现有数据的补充样本。该研究显着提高了从图像/文本数据到具有应用需求的关系数据的生成对抗网络的适用性，并有助于使用生成对抗网络进行约束感知关系数据生成领域的有限知识基础。研究结果，工具，软件代码和课程材料文件传播到研究社区，IT行业和用户，期望帮助域用户生成约束感知数据，以满足他们的业务需求。EAGER的这项研究开发了新的技术，使当前的生成对抗网络能够生成具有约束的现实关系数据。开发的框架增加了一个解码器的生成器，以产生数值和分类数据，并将约束条件的目标函数的生成器和搜索器引入多个鉴别器，以执行的要求约束。采用f-发散度分析了复杂约束条件下约束感知GAN框架的收敛性。然后，研究重点是在统一的框架下开发两个特定的模型，用于生成无歧视数据的公平GAN和用于在训练数据中只有阳性样本时生成阴性样本的互补GAN。该研究在准确性和收敛性方面对框架和两个特定模型进行了实证评估，实现并将开发的算法集成到开源深度学习软件系统TensorFlow中。开发的框架预计将推进对生成对抗网络的理论理解，两个特定的GAN模型预计将改善当前关于公平意识学习和欺诈检测的研究。特别地，公平GAN引入了基于GAN的公平数据生成的新方法，因为当前公平感知学习研究主要采用数据修改。免费的GAN通过生成补充样本并使经过训练的分类器能够准确地将异常样本从正常样本中分离出来，从而优于现有的一类分类模型。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Generative adversarial networks (GAN), which estimate the real data distribution through an adversarial game, have achieved great success in image generation and text generation. However, many decision models from real world applications are trained on relational data that contains both numerical and categorical attributes. Furthermore, real world applications often have legal or ethical requirements on the generated data, e.g., discrimination free or privacy preservation, or expect to generate complementary samples to their existing data that may only contain normal samples. The research significantly improves the applicability of generative adversarial networks from image/text data to relational data with application requirements and contributes to the limited base of knowledge in the area of using generative adversarial networks for constraint aware relational data generation. The findings, tools, software code, and curricular materials documents are disseminated to the research community, IT industry, and users, which expects to help domain users generate constraint aware data to meet their business needs. This EAGER research develops novel techniques that enable the current generative adversarial networks to generate realistic relational data with constraints. The developed framework adds a decoder to the generator to generate both numerical and categorical data and incorporates constraint terms into the objective functions of generator and discriminator or introduces multiple discriminators to enforce requirement constraints. The research adopts f-divergences to analyze the convergence of the constraint aware GAN framework when complex constraints are introduced. The research then focuses on developing under the unifying framework two specific models, fair GAN for generating discrimination-free data, and complementary GAN for generating negative samples when only positive samples are available in the training data. The research conducts empirical evaluations of the framework and two specific models in terms of accuracy and convergence, implements and integrates the developed algorithms into TensorFlow, an open source deep learning software system. The developed framework expects to advance theoretical understanding of generative adversarial networks and the two specific GAN models expect to improve the current research on fairness aware learning and fraud detection. In particular, the fair GAN introduces the new approach of fair data generation based on GAN as current fairness aware learning research mainly adopts data modification. The complimentary GAN outperforms existing one-class classification models for fraud detection by generating complementary samples and enabling the trained discriminator to accurately separate abnormal samples from normal ones.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.