Catastrophic forgetting is the tendency of a machine learning model to forget previously learned information when learning new information. This is a particular problem for artificial neural networks, which are often trained on large datasets of data.
When a neural network is trained on a new dataset, the weights of the network are adjusted to minimize the error on the new dataset. However, this can sometimes lead to the network forgetting the information that it learned from the previous dataset.
Catastrophic forgetting is a challenge for continual learning, which is the ability of a machine learning model to learn new information without forgetting the information that it has already learned.
Here is an example of catastrophic forgetting:
Suppose you train a neural network to classify images of cats and dogs. The network learns to perform this task well. However, then you decide to train the network to classify images of different types of flowers. As the network is trained on the new dataset, it starts to forget the information that it learned from the previous dataset. As a result, the network’s performance on the cat and dog classification task starts to decline.
There are a number of techniques that can be used to mitigate catastrophic forgetting. One common technique is to use regularization, which penalizes the network for changing its weights too much. Another technique is to use knowledge distillation, which involves training a new network on the old dataset and the new dataset. The new network is then able to learn the information from both datasets without forgetting the information from the old dataset.