  Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown
remarkable performance in most computer vision tasks. These tasks traditionally
use a fixed dataset, and the model, once trained, is deployed as is. Adding new
information to such a model presents a challenge due to complex training
issues, such as "catastrophic forgetting", and sensitivity to hyper-parameter
tuning. However, in this modern world, data is constantly evolving, and our
deep learning models are required to adapt to these changes. In this paper, we
propose an adaptive hierarchical network structure composed of DCNNs that can
grow and learn as new data becomes available. The network grows in a tree-like
fashion to accommodate new classes of data, while preserving the ability to
distinguish the previously trained classes. The network organizes the
incrementally available data into feature-driven super-classes and improves
upon existing hierarchical CNN models by adding the capability of self-growth.
The proposed hierarchical model, when compared against fine-tuning a deep
network, achieves significant reduction of training effort, while maintaining
competitive accuracy on CIFAR-10 and CIFAR-100.
