Continual Learning is the ability of neural networks to learn new tasks sequentially without forgetting previously learned knowledge — addressing the catastrophic forgetting problem that causes neural networks to lose old information when trained on new tasks.

Catastrophic Forgetting

• Standard neural networks: When fine-tuned on new task → overwrites weights that encoded old task.
• Example: Fine-tune ImageNet model on medical images → ImageNet accuracy drops 40%.
• Biological memory: Doesn't forget old skills when learning new ones (complementary learning systems).

Continual Learning Strategies

Regularization-Based:

• EWC (Elastic Weight Consolidation): Add penalty that protects important weights.
• $L = L_{new} + \lambda \sum_i F_i(\theta_i - \theta_i^*)^2$
• $F_i$: Fisher information — importance of parameter $i$ for old task.
• Important weights for old task → penalized from moving far.
• SI (Synaptic Intelligence): Online importance estimation during training.
• Limitation: Memory scales O(tasks × params) for task importance storage.

Memory Replay:

• Store examples from old tasks → replay during new task training.
• Experience Replay: Real stored samples. Memory cost: grows with tasks.
• Generative Replay (DGR): Train generative model on old data → replay synthetic samples.
• GDumb: Simply train on memory buffer — surprisingly competitive baseline.

Architecture-Based:

• Progressive Neural Networks: New column per task, lateral connections from old columns.
• Zero forgetting, but grows in size.
• PackNet: Prune old task → use freed capacity for new task.

Prompt-Based Continual Learning:

• Freeze pretrained model; learn small prompts per task.
• L2P (Learning to Prompt): Shared prompt pool — tasks select relevant prompts.
• No forgetting of pretrained features; task-specific adaptation via prompts.

Continual learning is a fundamental requirement for AI systems deployed in changing environments — industrial robots learning new assembly tasks, medical models adapting to new diseases, and personal assistants adapting to individual users all require learning new things without erasing old knowledge.