Overcoming Catastrophic Forgetting in Neural Networks

What is Catastrophic Forgetting?

Catastrophic forgetting is a phenomenon that occurs when a neural network is trained sequentially on multiple tasks and forgets how to perform previously learned tasks as it learns new ones.

Why is Catastrophic Forgetting a Problem?

Catastrophic forgetting is a problem because it prevents neural networks from being used for many real-world applications that require them to learn and perform multiple tasks.

How to Overcome Catastrophic Forgetting

There are a number of different techniques that can be used to overcome catastrophic forgetting. One common technique is to use a "replay" mechanism, which stores a subset of the training data from previous tasks and replays it to the network as it learns new tasks.

Another technique is to use a "regularization" term that penalizes the network for forgetting previously learned tasks.

Finally, it is also possible to use a "meta-learning" approach, which trains the network to learn how to learn new tasks without forgetting old ones.

Conclusion

Catastrophic forgetting is a serious problem that can prevent neural networks from being used for many real-world applications. However, there are a number of different techniques that can be used to overcome catastrophic forgetting, and these techniques are making it possible to develop neural networks that can learn and perform multiple tasks without forgetting old ones.

Additional Resources

• Overcoming Catastrophic Forgetting in Neural Networks
• Regularization Techniques for Neural Networks
• Meta-Learning for Neural Networks