Abstract
In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions.
DVAE is designed to alleviate the class imbalance
by explicitly learning class boundaries between
training samples, and uses learned class boundaries
to guide the feature learning and sample generation.
To learn class boundaries, DVAE learns a latent
two-component mixture distributor, conditioned by
the class labels, so the latent features can help differentiate minority class vs. majority class samples.
In order to balance the training data for deep learning to emphasize on the minority class, we combine
DVAE and generative adversarial networks (GAN)
to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons con-
firm that DVAAN significantly alleviates the class
imbalance and delivers accurate models for deep
learning from imbalanced data.