Continual Learning Techniques to Reduce Forgetting: A Comparative Study

A key aspect of artificial intelligence is continual learning. The capacity of a model to incrementally learn from a new task and adapt to it without losing the previous knowledge. A fundamental challenge that comes to successful continual learning is catastrophic forgetting; while learning a new task the model forgets the previously learned tasks. Various approaches to mitigate forgetting and enhance continual learning have been discussed across benchmark datasets such as MNIST, CIFAR- 10, and Permuted MNIST. we focus mainly on replay, regularization, and modular architecture-based methods to prevent forgetting. Regularization methods penalize the significant change in parameters during the learning of new tasks while maintaining crucial parameters of previously completed tasks. Replay-based methods store samples of earlier tasks in explicit memory or generate pseudo-samples with the help of generative models. Modular architecture-based methods partition the neural architecture into task-specific modules to prevent interference between tasks, to effectively address catastrophic forgetting. This paper presents a comparative study of the key continual learning methods that reduce catastrophic forgetting. The findings demonstrate the trade-offs between different methods. We examine the efficiency and effectiveness of each method and propose a hybrid technique to prevent catastrophic forgetting. Our comparative analysis emphasizes the significance of choosing the best method depending on the specific requirement of the learning environment, the task complexity, and resource constraints.