  Supervised learning from training data with imbalanced class sizes, a
commonly encountered scenario in real applications such as anomaly/fraud
detection, has long been considered a significant challenge in machine
learning. Motivated by recent progress in curriculum and self-paced learning,
we propose to adopt a semi-supervised learning paradigm by training a deep
neural network, referred to as SelectNet, to selectively add unlabelled data
together with their predicted labels to the training dataset. Unlike existing
techniques designed to tackle the difficulty in dealing with class imbalanced
training data such as resampling, cost-sensitive learning, and margin-based
learning, SelectNet provides an end-to-end approach for learning from important
unlabelled data "in the wild" that most likely belong to the under-sampled
classes in the training data, thus gradually mitigates the imbalance in the
data used for training the classifier. We demonstrate the efficacy of SelectNet
through extensive numerical experiments on standard datasets in computer
vision.
