基于隐变量后验生成对抗网络的不平衡学习

Unbalanced Learning of Generative Adversarial Network Based on Latent Posterior

针对现有不平衡分类问题中过采样方法不能充分利用数据概率密度分布的问题,提出了一种基于隐变量后验生成对抗网络的过采样(LGOS)算法.该方法利用变分自编码求取隐变量的近似后验分布,生成器能有效估计数据真实概率分布,在隐空间中采样克服了生成对抗网络采样过程的随机性,并引入边缘分布自适应损失和条件分布自适应损失提升生成数据质量.此外,将生成样本当作源领域样本放入迁移学习框架中,提出了改进的基于实例的迁移学习(TrWSBoost)分类算法,引入了权重缩放因子,有效解决了源领域样本权重收敛过快、学习不充分的问题.实验结果表明,提出的方法在分类问题各指标上的表现明显优于现有方法.

Based on the problem that the oversampling method in the existing unbalanced classification problem cannot fully utilize the data probability density distribution,a method named latent posterior based generative adversarial network for oversampling(LGOS)was proposed.This method used variational auto-encoder to obtain the approximate posterior distribution of latent variable and generation network could effectively estimate the true probability distribution function of the data.The sampling in the latent space could overcome the randomness of generative adversarial network.The marginal distribution adaptive loss and the conditional distribution adaptive loss were introduced to improve the quality of generated data.Besides,the generated samples as source domain samples were put into the transfer learning framework,the classification algorithm of transfer learning for boosting with weight scaling(TrWSBoost)was proposed,and the weight scaling factor was introduced,which effectively solved the problem that the weight of source domain samples converge too fast and lead to insufficient learning.The experimental results show that the proposed method is superior to the existing oversampling method in the performance of common metrics.