Efficient Augmentation for Imbalanced Deep Learning

Understand

Deep learning models tend to memorize training data, which hurts their ability to generalize to under-represented classes.

• We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes.
• This insight enables us to design an efficient three-phase CNN training framework for imbalanced data.
• The framework involves training the network end-to-end on imbalanced data to learn accurate feature embeddings, performing data augmentation in the learned embedded space to balance the train distribution, and fine-tuning the classifier head on the embedded balanced training data.