In recent years, Convolutional Neural Networks (CNNs) have shown remarkable performance in many computer vision tasks such as object recognition and detection. However, complex training issues, such as "catastrophic forgetting" and hyper-parameter tuning, make incremental learning in CNNs a difficult challenge. In this paper, we propose a hierarchical deep neural network, with CNNs at multiple levels, and a corresponding training method for incremental learning. The network grows in a tree-like manner to accommodate the new classes of data without losing the ability to identify the previously trained classes. The proposed network was tested on CIFAR-100 and reported 60.46% accuracy and 20% reduction in training effort as compared to retraining final layers of a deep network. The network organizes the incoming classes of data into feature-driven super-classes and improves upon existing hierarchical CNN models by adding the capability of self-growth.
Another hierarchical approach was proposed in Roy et al. REF where the network grows in a tree-like manner to accommodate the new classes.
Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning
Neural networks : the official journal of the International Neural Network Society
Neural networks : the official journal of the International Neural Network Society
Computer Science Medicine Mathematics