Gradient Episodic Memory
Gradient Episodic Memory (GEM) is a machine learning technique designed to address catastrophic forgetting in continual learning scenarios, where models are trained sequentially on new tasks without forgetting previously learned ones. It works by storing a small episodic memory of past task examples and using quadratic programming to constrain gradient updates, ensuring that new learning does not interfere with old knowledge. This approach enables neural networks to accumulate knowledge over time while maintaining performance on earlier tasks.
Developers should learn GEM when building AI systems that need to adapt to new data or tasks over time without retraining from scratch, such as in robotics, autonomous vehicles, or personalized recommendation systems. It is particularly useful in scenarios where data arrives sequentially and storage of all past data is impractical, as it mitigates catastrophic forgetting efficiently with minimal memory overhead. This makes it a key technique for lifelong learning applications where models must evolve continuously.