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We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regular-izing large fully-connected layers within neu-ral networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropCon-nect instead sets a randomly selected sub-set of weights within the network to zero. Each unit thus receives input from a ran-dom subset of units in the previous layer. We derive a bound on the generalization per-formance of both Dropout and DropCon-nect. 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 mul-tiple DropConnect-trained models. 1.