Robust Continual Learning

Robust continual learning aims to enable artificial intelligence systems to learn new tasks sequentially without forgetting previously acquired knowledge, a challenge known as catastrophic forgetting. Current research focuses on improving model robustness against various data issues, including noisy labels, outliers, and adversarial attacks, often employing techniques like Bayesian regularization, low-rank approximations, and data sampling strategies to manage model parameters and prevent performance degradation. These advancements are crucial for developing reliable and adaptable AI systems applicable to real-world scenarios such as robotics and autonomous vehicles where continuous learning from dynamic data streams is essential.