Pseudo-Feature Generation for Imbalanced Data Analysis in Deep Learning

We generate pseudo-features by multivariate probability distributions obtained from feature maps in a low layer of trained deep neural networks. Then, we virtually augment the data of minor classes by the pseudo-features in order to overcome imbalanced data problems. Because all the wild data are imbalanced, the proposed method has the possibility to improve the ability of DNN in a broad range of problems