What is the purpose of Elastic Weight Consolidation (EWC)?

Elastic Weight Consolidation (EWC) is specifically designed to prevent catastrophic forgetting in neural networks. When a model is trained sequentially on different tasks, it can lose the knowledge acquired from earlier tasks due to updates from training on new data. EWC addresses this by adding a regularization term to the loss function during training, which essentially helps to "consolidate" important weights from previous tasks. This regularization discourages the model from drastically changing weights that are crucial for tasks it has already learned, thereby preserving previously acquired knowledge while still allowing for the learning of new tasks.

This technique utilizes a Fisher information matrix to determine which weights are important for maintaining performance on previous tasks, allowing the model to be fine-tuned on new tasks without significant interference with what it has already learned. Therefore, the primary purpose of EWC lies in its ability to mitigate the issue of catastrophic forgetting, enabling more robust continual learning in AI systems.