Class-Incremental Learning (CIL) is a learning paradigm where a model gradually learns new classes over time.
Unlike traditional learning, where all classes are learned at once, CIL introduces data incrementally,
requiring the model to learn new classes without forgetting previously learned ones.
This challenge is known as the Catastrophic Forgetting problem.
Storing and training on all data at once can be inefficient and unrealistic.
In cases where data is too large or contains sensitive information,
incremental learning through CIL becomes essential.
CIL plays a crucial role in maintaining model performance in dynamic learning environments,
allowing it to retain past knowledge while adapting to new information. 🚀