Continual Learning in Deep Neural Network by Using a Kalman Optimiser

TL;DR

This paper introduces a Kalman Optimiser that enables deep neural networks to learn new tasks sequentially without forgetting previous knowledge by dividing the network into long-term and short-term memory units.

Contribution

The novel Kalman Optimiser approach effectively separates long-term and short-term memory in neural networks for continual learning, improving adaptation without forgetting.

Findings

Successfully applied on MNIST, CIFAR10, CIFAR100 datasets

Outperforms baseline models in continual learning tasks

Prevents catastrophic forgetting during sequential learning

Abstract

Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning models. Most of the conventional deep learning models are not capable of learning new tasks sequentially in one model without forgetting the previously learned ones. We address this issue by using a Kalman Optimiser. The Kalman Optimiser divides the neural network into two parts: the long-term and short-term memory units. The long-term memory unit is used to remember the learned tasks and the short-term memory unit is to adapt to the new task. We have evaluated our method on MNIST, CIFAR10, CIFAR100 datasets and compare our results with state-of-the-art baseline models. The results show that our approach enables the model to continually learn and adapt to the new changes without forgetting the previously learned tasks.