Class Incremental Learning

Class incremental learning (CIL) focuses on training machine learning models to continuously learn new classes of data without forgetting previously learned ones, a crucial challenge for real-world applications with evolving data streams. Current research emphasizes techniques like dynamic model architectures (e.g., adding task-specific adapters), generative replay methods to synthesize past data, and the use of pre-trained models to leverage existing knowledge. These advancements aim to improve accuracy and fairness while addressing issues like catastrophic forgetting and data imbalance, impacting fields such as medical image analysis, sound source localization, and personalized AI systems.