  This paper addresses a boosting method for mapping functionality of neural
networks in visual recognition such as image classification and face
recognition. We present reversible learning for generating and learning latent
features using the network itself. By generating latent features corresponding
to hard samples and applying the generated features in a training stage,
reversible learning can improve a mapping functionality without additional data
augmentation or handling the bias of dataset. We demonstrate an efficiency of
the proposed method on the MNIST,Cifar-10/100, and Extremely Biased and poorly
categorized dataset (EBPC dataset). The experimental results show that the
proposed method can outperform existing state-of-the-art methods in visual
recognition. Extensive analysis shows that our method can efficiently improve
the mapping capability of a network.
