  Herein, we generate pseudo-features based on the multivariate probability
distributions obtained from the feature maps in layers of trained deep neural
networks. Further, we augment the minor-class data based on these generated
pseudo-features to overcome the imbalanced data problems. The proposed method,
i.e., cavity filling, improves the deep learning capabilities in several
problems because all the real-world data are observed to be imbalanced.
