Incremental Learning

Incremental learning aims to enable machine learning models to continuously acquire new knowledge from sequential data streams without forgetting previously learned information, a challenge known as catastrophic forgetting. Current research focuses on developing algorithms and model architectures, such as those employing knowledge distillation, generative replay, and various regularization techniques, to address this issue across diverse applications like image classification, gesture recognition, and medical image analysis. This field is significant because it moves machine learning closer to human-like continuous learning capabilities, with potential impacts on personalized medicine, robotics, and other areas requiring adaptation to evolving data.