Adaptation of Artificial Neural Networks Avoiding Catastrophic Forgetting

IJCNN, pp. 1554-1561, 2006.

Cited by: 8|Views20
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Abstract:

In connectionist learning, one relevant problem is "catastrophic forgetting" that may occur when a network, trained with a large set of patterns, has to learn new input patterns, or has to be adapted to a different environment. The risk of catastrophic forgetting is particularly high when a network is adapted with new data that do not ade...More

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