Abstract:Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of subnetworks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyperparameter over different layers during the whole training process. … Show more
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