EnsembleNet: A novel architecture for Incremental Learning

Suri Bhasker Sri Harsha, Y Kalidas

Abstract: Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, it cannot be easily trained to do new tasks as it leads to catastrophic forgetting of the previously learned tasks. We propose here a novel architecture called EnsembleNet that accommodates for newer classes of data without having to retrain previously trained sub-models. The novelty of our model lies in the fact that only a small portion of the network has to be retrained which makes it extremely computational efficient and also results in high performance compared to other architectures in the literature. We demonstrated our model on MNIST Handwritten digits, MNIST Fashion, and CIFAR10 datasets. The proposed architecture was benchmarked against other models in the literature on Omega-new, Omega-base, Omega-all metrics for MNIST- Handwritten dataset. The experimental results show that Ensemble Net on overall outperformed every other model in the literature.