Continual Learning Task

Continual learning aims to enable artificial neural networks to acquire new knowledge sequentially without forgetting previously learned information, a challenge known as catastrophic forgetting. Current research focuses on mitigating this issue through various strategies, including architectural modifications like mixture-of-experts models and spiking neural networks, algorithmic improvements such as symmetric forward-forward algorithms and selective parameter updates, and bio-inspired approaches leveraging synaptic plasticity. Success in continual learning is crucial for developing more robust and adaptable AI systems capable of lifelong learning, impacting fields ranging from medical image analysis to natural language processing.