Which term describes the method that helps maintain previously learned knowledge when new data is introduced?

The appropriate term that describes the method for maintaining previously learned knowledge when new data is introduced is Incremental Learning. This approach allows a model to learn continuously from new data while retaining the knowledge acquired from previous data. Incremental learning focuses on adapting the model without the need to retrain from scratch, thus preventing the model from forgetting earlier data, a common issue known as catastrophic forgetting that can occur with traditional training methods.

In the context of incremental learning, new information is assimilated into the existing knowledge base, allowing the model to enhance its understanding and make better predictions based on an evolving dataset. This is particularly useful in dynamic environments where data can change or grow over time.

While options like Memory Network and Replay Buffer may relate to retaining knowledge in specific contexts, Incremental Learning is the most comprehensive term for the process described in the question. Memory Networks are designed for tasks involving long-term storage and retrieval of information, while Replay Buffers are commonly used in reinforcement learning to store past experiences for training. Generative Adversarial Networks, on the other hand, are a framework for generating synthetic data and do not inherently focus on maintaining learned knowledge across new data inputs.