Unbalanced Learning of Generative Adversarial Network Based on Latent Posterior

Abstract

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.

Keywords

• unbalanced classification
• generative adversarial network
• latent variable
• transfer learning