Notes

Regularization Methods Regularization methods are used in continual learning to prevent catastrophic forgetting, which occurs when a model forgets previously learned tasks when learning new tasks.

Elastic Weight Consolidation (EWC) Elastic Weight Consolidation (EWC) aims to keep the parameters of a neural network that are important for previous tasks fixed while allowing the parameters that are important for new tasks to change. This is done by adding a penalty term to the loss function based on the difference between the current parameters and the parameters that were optimal for the previous tasks.The main idea of EWC is to keep the parameters of a neural network that are important for previous tasks fixed while allowing the parameters that are important for new tasks to change. This is done by adding a penalty term to the loss function based on the difference between the current parameters and the parameters that were optimal for the previous tasks. The importance of each parameter is measured by the Fisher information matrix, which quantifies the amount of information that the parameter contains about the task.

Synaptic Intelligence (SI) Synaptic Intelligence (SI) aims to keep the parameters that were important for previous tasks fixed by adjusting the learning rate of each parameter based on how much it has changed during previous tasks. The main idea of SI is to adjust the learning rate of each parameter in a neural network based on how much the parameter has changed during previous tasks. The authors propose a measure of the "importance" of each parameter, which is based on the magnitude of the gradient of the parameter with respect to the loss function during previous tasks. Parameters that have had a large gradient in the past are considered more important and have a lower learning rate, while parameters that have had a small gradient in the past have a higher learning rate. The authors test SI on a variety of image classification tasks and show that it outperforms EWC and other baselines. The paper also introduces a novel method for evaluating the performance of continual learning algorithms called "learning progress".