Discriminative Features for Incremental Learning Classifier

Abstract

An important problem in artificial intelligence is to develop an efficient system that can adapt to new knowledge in an incremental manner without forgetting previously learned knowledge. Although Convolutional Neural Networks (CNNs) are good at learning strong classifier and discriminative features, CNNs can not perform well in incremental classifier learning due to the catastrophic forgetting problem in the retraining process. In this paper, we propose a novel yet extremely simple approach to enhance the discriminative property of features for incremental classifier learning. We build a network for the universal feature space in which a group of image classes have intra-class compactness and inter-class separability. And, we model each incremental class to have a maximum margin from the rest of the models in universal space. Experiments are conducted on CIFAR-100 dataset and IMage Database for Context Aware Advertisement (IMDB-CAA) we collected. The results demonstrate the superiority of our approach, improving performance on CIFAR-100 dataset over state-of-the-art incremental learning systems. Furthermore, experiments on few-short incremental learning setting show very promising performance although we use only 4% of training samples on CIFAR-100 dataset.