Humans regularly perform new learning without losing memory for previous information, but neural network models suffer from the phenomenon of catastrophic forgetting in which new learning impairs prior function. A recent article presents an algorithm that spares learning at synapses important for previously learned function, reducing catastrophic forgetting.

中文翻译：

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避免灾难性遗忘

人类定期进行新的学习，而不会失去对先前信息的记忆，但神经网络模型会遭受灾难性遗忘现象，其中新的学习会损害先验功能。最近的一篇文章提出了一种算法，该算法可以避免在对先前学习的功能很重要的突触上进行学习，从而减少灾难性的遗忘。