Continual Learning: Models That Keep Learning
Train models on new data without forgetting old knowledge. Continual learning strategies for evolving AI systems.
TL;DR
Continual learning updates models with new data while retaining old knowledge. Strategies: regularization (EWC), replay (store old examples), or architecture methods (progressive networks).
The problem: catastrophic forgetting
Fine-tuning on new data often erases previous knowledge. Model "forgets" original tasks.
Solutions
Regularization: Penalize changes to important weights (EWC, SI)
Replay: Mix old and new data during training
Architectural: Add new parameters for new tasks (progressive networks)
Meta-learning: Learn to learn continually
Use cases
- Personalization (adapt to user over time)
- Domain adaptation (new industries, languages)
- Evolving knowledge (update facts)
Challenges
- Balancing old vs new knowledge
- Storage for replay
- Computational cost
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Key Terms Used in This Guide
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
Machine Learning (ML)
A way to train computers to learn from examples and data, instead of programming every rule manually.
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