论文标题
用各种自动编码器重新编程Fairgans：一种新的转移学习模型
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model
论文作者
论文摘要
公平感知的甘恩（Fairgans）利用生成的对抗网络（GAN）的机制对生成的数据强加公平，使他们摆脱了不同的影响和不同的治疗方法。鉴于该模型的优势和性能，我们介绍了一个新颖的学习框架，将预训练的Fairgan转移到其他任务中。此重编程过程的目的是维护Fairgan的主要数据，分类实用程序和数据公平性，同时扩大其适用性和易用性。在本文中，我们介绍了将原始体系结构调整到该新框架（尤其是变异自动编码器的使用）所需的技术扩展，并讨论新模型的好处，权衡和限制。
Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment. Given the model's advantages and performance, we introduce a novel learning framework to transfer a pre-trained FairGAN to other tasks. This reprogramming process has the goal of maintaining the FairGAN's main targets of data utility, classification utility, and data fairness, while widening its applicability and ease of use. In this paper we present the technical extensions required to adapt the original architecture to this new framework (and in particular the use of Variational Auto-Encoders), and discuss the benefits, trade-offs, and limitations of the new model.
