Improved method for learning data imbalance in gender classification model using DA-FSL

As the deep learning technology grows, the accuracy of the training data to improve the model becomes important. If there are not enough learning data between classes, there is a problem that the accuracy of the deep learning model is greatly reduced. In this paper, we propose a method to solve data imbalance caused by the difficulty of collecting learning data through DA-FSL(Data Augmentation based Few-Shot Learning). The proposed method is to separate the class with the data imbalance and the normal class, and to re-learn by creating the data of the data imbalance class through DA-FSL. It adopts GAN(Generative Adversarial Network) architecture, then initialize through mapping network to improve the generation accuracy and speed of new latent vector. The purpose of this paper is to verify whether the data imbalance of gender classification model can be solved through the experiments applied by the proposed method and to prove its effectiveness.