Progressive Convolutional Neural Network for Incremental Learning

In this paper, we present a novel incremental learning technique to solve the catastrophic forgetting problem observed in the CNN architectures. We used a progressive deep neural network to incrementally learn new classes while keeping the performance of the network unchanged on old classes. The incremental training requires us to train the network only for new classes and fine-tune the final fully connected layer, without needing to train the entire network again, which significantly reduces the training time. We evaluate the proposed architecture extensively on image classification task using Fashion MNIST, CIFAR-100 and ImageNet-1000 datasets. Experimental results show that the proposed network architecture not only alleviates catastrophic forgetting but can also leverages prior knowledge via lateral connections to previously learned classes and their features. In addition, the proposed scheme is easily scalable and does not require structural changes on the network trained on the old task, which are highly required properties in embedded systems.

Automated summary

This paper introduces a novel incremental learning technique to address the catastrophic forgetting problem in CNN architectures, utilizing a progressive deep neural network to learn new classes incrementally while maintaining performance on old classes. Extensively evaluated on various datasets, experimental results demonstrate that the proposed network architecture not only alleviates catastrophic forgetting but also leverages prior knowledge through lateral connections to previously learned classes and their features.