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2018/2/26 · 静态超网络为前馈网络生成权重. 动态超网络为循环网络生成权重. 这篇论文的主要成果是 挑战 循环网络的权重共享范式。. 我们在一个大型LSTM网络中嵌入了一个小型的超网络，因此，主LSTM网络可以根据超网络修改自己的权重。. 在这一例子中，超网络也是一个
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所谓hypernetworks，即用一个网络生成另一个网络。被生成的网络被称为main network. 作为一种meta-learning的方法，其优点在于，可以限制模型的参数空间的大小。 模型建立 文章中，作者提出静态与动态的两种hypernetwork。
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2019/6/3 · Continual learning with hypernetworks. Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, we present a novel approach based on task-conditioned hypernetworks, i.e., networks that generate the weights of a target model based on task identity.
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2020/2/11 · 本文提出了一个任务条件超网络( task-conditioned hypernetworks)，用于给给定任务id的主网络(主网络)B 生成权重。 论文的贡献： 拓宽了超网络的应用领域，即使用超网络实现了CL。基于超网络的CL方案不仅在标准CL基准上达到了最佳，而且在长序列任务
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HyperNetworks PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet. The code is primarily for CIFAR-10 but it's super easy to use it for any other dataset. It's also very easy to use it for ResNet architectures of different depths. (Please cite
Published as a conference paper at ICLR 2020 CONTINUAL LEARNING WITH HYPERNETWORKS Johannes von Oswald*, Christian Henning*, Benjamin F. Grewe, João Sacramento *Equal contribution Institute of Neuroinformatics University of Zürich and ETH Zürich
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