Adaptive Continual Learning

Adaptive continual learning aims to enable artificial intelligence systems to continuously learn new information without forgetting previously acquired knowledge, mirroring human learning capabilities. Current research focuses on mitigating "catastrophic forgetting" through techniques like meta-learning, dynamic network architectures (including spiking neural networks), and adaptive memory management strategies, often applied within frameworks like meta-continual learning. This field is crucial for developing robust and efficient AI systems capable of adapting to evolving data streams in real-world applications such as autonomous driving, video analytics, and audio deepfake detection, where continuous learning and adaptation are essential.