Understanding Continual Learning in Neural Networks

The Problem: Neural networks suffer from catastrophic forgetting — when trained on new tasks, they lose performance on previously learned ones. This is a fundamental limitation preventing AI systems from learning continuously like humans do.

The Idea: The key insight is that not all memories are equally important. Difficult samples — those near decision boundaries — are more informative for maintaining performance. We can be strategic about what we remember and how we protect learned knowledge.

My Solution: DREAM (Difficulty-REplay-Augmented Memory) — a method that prioritizes difficult samples in replay buffers, achieving better continual learning performance with smaller memory footprints. Combined with regularization techniques like EWC to protect critical parameters.

The Vision: AI systems that learn like humans: continuously, efficiently, and without forgetting. This is essential for real-world deployment — a self-driving car must adapt to new road conditions without forgetting how to handle familiar ones.