Imbalanced data

• The Impact of Imbalanced Training Data for Convolutional Neural Networks

Following this, oversampling is used on the imbalanced training sets to increase the performances to that of the balanced set. It is concluded that oversampling is a viable way to counter the impact of imbalances in the training data.

• Deep neural network with unbalanced classes