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Deep neural networks have become the method of choice for solving manyclassification tasks, largely because they can fit very complex functionsdefined over raw data. The downside of such powerful learners is the danger ofoverfit. In this paper, we introduce a novel ensemble classifier for deepnetworks that effectively overcomes overfitting by combining models generatedat specific intermediate epochs during training. Our method allows for theincorporation of useful knowledge obtained by the models during the overfittingphase without deterioration of the general performance, which is usually missedwhen early stopping is used. To motivate this approach, we begin with thetheoretical analysis of a regression model, whose prediction -- that thevariance among classifiers increases when overfit occurs -- is demonstratedempirically in deep networks in common use. Guided by these results, weconstruct a new ensemble-based prediction method, where the prediction isdetermined by the class that attains the most consensual prediction throughoutthe training epochs. Using multiple image and text classification datasets, weshow that when regular ensembles suffer from overfit, our method eliminates theharmful reduction in generalization due to overfit, and often even surpassesthe performance obtained by early stopping. Our method is easy to implement andcan be integrated with any training scheme and architecture, without additionalprior knowledge beyond the training set. It is thus a practical and useful toolto overcome overfit. Code is available athttps://github.com/uristern123/United-We-Stand-Using-Epoch-wise-Agreement-of-Ensembles-to-Combat-Overfit.

United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit