论文标题
少数族裔的退火遗传gan
Annealing Genetic GAN for Minority Oversampling
论文作者
论文摘要
克服阶级不平衡问题的关键是准确捕获少数民族的分布。生成的对抗网络（GAN）显示出一些潜在的潜力来解决阶级不平衡问题，因为它们有能力在给定足够的培训数据样本的情况下重现数据分布。但是，一个或多个课程的稀缺样本仍然对甘斯学习少数群体的准确分布构成了巨大的挑战。在这项工作中，我们提出了一种退火遗传GAN（AGGAN）方法，该方法旨在仅使用有限的数据样本来重现最接近少数类别的分布。我们的Aggan将gan的培训作为进化过程进行了翻新，该过程结合了模拟退火的机制。特别是，发电机使用不同的培训策略来产生多个后代并保持最佳状态。然后，我们使用模拟退火中的大都市标准来决定是否应该更新发电机的最佳后代。由于Metropolis标准允许一定机会接受更糟糕的解决方案，因此它使我们的Aggan能够远离当地的最佳距离。根据有关多个不平衡图像数据集的理论分析和实验研究，我们证明拟议的培训策略可以使我们的Aggan能够从稀缺样本中重现少数群体的分布，并为阶级不平衡问题提供有效而强大的解决方案。
The key to overcome class imbalance problems is to capture the distribution of minority class accurately. Generative Adversarial Networks (GANs) have shown some potentials to tackle class imbalance problems due to their capability of reproducing data distributions given ample training data samples. However, the scarce samples of one or more classes still pose a great challenge for GANs to learn accurate distributions for the minority classes. In this work, we propose an Annealing Genetic GAN (AGGAN) method, which aims to reproduce the distributions closest to the ones of the minority classes using only limited data samples. Our AGGAN renovates the training of GANs as an evolutionary process that incorporates the mechanism of simulated annealing. In particular, the generator uses different training strategies to generate multiple offspring and retain the best. Then, we use the Metropolis criterion in the simulated annealing to decide whether we should update the best offspring for the generator. As the Metropolis criterion allows a certain chance to accept the worse solutions, it enables our AGGAN steering away from the local optimum. According to both theoretical analysis and experimental studies on multiple imbalanced image datasets, we prove that the proposed training strategy can enable our AGGAN to reproduce the distributions of minority classes from scarce samples and provide an effective and robust solution for the class imbalance problem.
