Editing Continual Learning (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Continual learning''' — Training a model on a sequence of tasks or data streams over time without forgetting prior knowledge.
* '''Catastrophic forgetting''' — The tendency of neural networks to abruptly lose previously learned knowledge when trained on new data.
* '''Task-incremental learning''' — The model learns a sequence of distinct tasks, with task identity known at inference time.
* '''Class-incremental learning''' — The model incrementally learns new classes; task identity is not given at test time (harder).
* '''Domain-incremental learning''' — Same task but data distribution changes over time (e.g., new image styles).
* '''Plasticity''' — The model's ability to learn new information quickly.
* '''Stability''' — The model's ability to retain previously learned information.
* '''Stability-plasticity dilemma''' — The fundamental trade-off: high plasticity enables fast learning but causes forgetting; high stability prevents forgetting but blocks new learning.
* '''Elastic Weight Consolidation (EWC)''' — A regularization approach that penalizes changes to parameters important for previous tasks.
* '''Progressive Neural Networks''' — Freeze previous task columns and add new lateral connections for new tasks; no forgetting but grows with each task.
* '''Experience replay''' — Storing a small buffer of past examples and mixing them into training on new tasks.
* '''Dark Experience Replay (DER)''' — Stores soft targets (logits) from past predictions, not just input-output pairs.
* '''PackNet''' — Prunes and packs model weights for multiple tasks into the same fixed-size network.
* '''Fisher information matrix''' — Used in EWC to measure parameter importance to previous tasks.
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