Deep Class Incremental Learning

Deep class incremental learning (DCIL) focuses on enabling artificial intelligence systems to continuously learn new classes of data without forgetting previously acquired knowledge, a phenomenon known as catastrophic forgetting. Current research emphasizes strategies to mitigate this forgetting, exploring approaches such as exemplar-based methods, memory-based methods, and network-based methods, often incorporating open-set recognition to handle misclassified novel samples. This field is crucial for developing robust and adaptable AI systems capable of handling real-world scenarios with evolving data streams, impacting applications ranging from robotics and financial modeling to medical image analysis.