## TL;DR
Continual learning enables neural networks to learn new tasks without forgetting previous ones. From EWC's Fisher-based regularization to MESU's Bayesian uncertainty approach, the field targets the fundamental challenge of catastrophic forgetting.

## Core Explanation
Catastrophic forgetting: when a neural network trains on Task B after Task A, gradient updates for B overwrite weights that encode A's knowledge, causing sudden performance drop on A. Three solution families: (1) Regularization (EWC, SI) — penalize weight changes on important parameters; (2) Replay (Experience Replay, GEM) — store and replay previous task samples; (3) Architecture (Progressive Networks) — grow capacity for each task.

## Detailed Analysis
EWC identifies important weights via the diagonal of the Fisher Information Matrix, approximated from gradients of the previous task's loss. MESU advances this by maintaining per-parameter uncertainty estimates — high-uncertainty parameters remain plastic while low-uncertainty ones consolidate, mimicking biological synaptic metaplasticity. Dynamic architecture methods (PackNet) prune and reallocate capacity.

## Further Reading
- ContinualAI Community and Avalanche Library
- "A Continual Learning Survey" (Parisi et al.)
- NeurIPS 2024 Continual Learning Workshop