Continual Learning in Deep Neural Network by Using a Kalman Optimiser
This addresses the challenge of sequential task learning without forgetting for AI systems, though it appears incremental as it builds on existing continual learning methods with a specific optimization approach.
The paper tackles the problem of catastrophic forgetting in continual learning for deep neural networks by introducing a Kalman Optimiser that separates long-term and short-term memory units, achieving results on MNIST, CIFAR10, and CIFAR100 datasets that show the model can learn new tasks without forgetting previous ones.
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.