Papers › Regularization of Neural Networks using DropConnect

Regularization of Neural Networks using DropConnect

13 Jun 2013ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 2013 6archive 2025-07-28

Li Wan, Matthew Zeiler, Sixin Zhang, Yann Lecun, Rob Fergus

We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.

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Introduced by this paper: DropConnect

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