Discriminative Sample Generation for Deep Imbalanced Learning

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 confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.

Authors

• Ting Guo
• Xingquan Zhu Florida Atlantic University
• Yang Wang
• Fang Chen School of Biomedical Engineering, and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

Keywords

• Machine Learning: Data Mining
• Machine Learning: Deep Learning
• Machine Learning: Learning Generative Models