catastrophic forgetting prevention

**Catastrophic Forgetting Prevention** encompasses **techniques that prevent a neural network from losing previously learned knowledge when trained on new tasks** — a critical challenge in continual learning, transfer learning, and fine-tuning scenarios. **Key Prevention Techniques** - **Regularization-Based**: - **EWC** (Elastic Weight Consolidation): Penalize changes to weights important for previous tasks. - **L2-SP**: Regularize toward the pre-trained weights. - **Architecture-Based**: - **Progressive Networks**: Add new columns for new tasks, freeze old columns. - **PackNet**: Prune and freeze subnetworks for each task. - **Replay-Based**: - **Experience Replay**: Store and replay examples from previous tasks. - **Generative Replay**: Use a generative model to synthesize past data. **Why It Matters** - **Continual Learning**: The #1 obstacle to lifelong learning in neural networks. - **Fine-Tuning**: Aggressive fine-tuning on small datasets can destroy pre-trained knowledge. - **Practical**: Any system deployed over time (recommendation engines, autonomous vehicles) faces catastrophic forgetting. **Catastrophic Forgetting Prevention** is **the art of learning new tricks without forgetting old ones** — the central challenge in making neural networks truly adaptable over time.