Incremental Classifier

Incremental classifiers are machine learning models designed to learn new classes or domains sequentially, without catastrophic forgetting of previously acquired knowledge. Current research focuses on developing robust architectures and algorithms, such as those incorporating consolidation techniques at both representation and classifier levels, and employing methods like embedding distillation and generative models to mitigate forgetting. This area is crucial for handling real-world data streams exhibiting concept drift and for applications requiring continuous learning from evolving data, such as in object detection and anomaly detection in dynamic systems. The development of effective incremental classifiers has significant implications for various fields, including robotics, autonomous driving, and online monitoring systems.