Overcoming catastrophic forgetting in neural networks
Overcoming catastrophic forgetting in neural networks
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DOI：
10.1073/pnas.1611835114
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发表时间：
2017-03-28
影响因子：
11.1
通讯作者：
Hadsell, Raia
中科院分区：
文献类型：
--
作者：
Kirkpatricka, James;Pascanu, Razvan;Hadsell, Raia
以顺序方式学习任务的能力对人工智能的发展至关重要。到目前为止，神经网络还没有能力做到这一点，人们普遍认为灾难性遗忘是联结主义模型的一个不可避免的特征。我们证明，有可能克服这一限制，并训练网络，使其能够在长期没有经历过的任务上保持专业知识。我们的方法通过选择性地减慢对这些任务重要的权重的学习来记住旧任务。我们证明了我们的方法是可扩展的和有效的解决一组分类任务的基础上手写的数字数据集，并通过学习几个雅达利2600游戏顺序。
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.