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State-of-the-Art Techniques in Artificial Intelligence for Continual Learning: A Review

Salami Bukola, Haataja Keijo, Toivanen Pekka

Annals of Computer Science and Information Systems

2021

Vol. 26

23--32

Artificial neural networks are used in many state-of-the-art systems for perception, and they thrive at solving classification problems, but they lack the ability to transfer that learning to a new task. Human and animals both have the capability of acquiring knowledge and transfer them continually throughout their lifespan. This term is known as continual learning. Continual learning capabilities are important to Artificial Neural Network in the real world especially with the increasing stream of data. However, it remains a challenge to be achieved because they are prone to catastrophic forgetting. Fixing this problem is critical, so that ANN incrementally learn and improve when deployed to real life situations. In this paper, we did a taxonomy of continual learning first in human by introducing plasticity-stability dilemma and some other learning and forgetting process in the brain. We did a state-of-the-art review of three different approaches to continual learning to mitigate catastrophic forgetting.