  Deep neural networks have become the method of choice for solving many
classification tasks, largely because they can fit very complex functions
defined over raw data. The downside of such powerful learners is the danger of
overfit. In this paper, we introduce a novel ensemble classifier for deep
networks that effectively overcomes overfitting by combining models generated
at specific intermediate epochs during training. Our method allows for the
incorporation of useful knowledge obtained by the models during the overfitting
phase without deterioration of the general performance, which is usually missed
when early stopping is used. To motivate this approach, we begin with the
theoretical analysis of a regression model, whose prediction -- that the
variance among classifiers increases when overfit occurs -- is demonstrated
empirically in deep networks in common use. Guided by these results, we
construct a new ensemble-based prediction method, where the prediction is
determined by the class that attains the most consensual prediction throughout
the training epochs. Using multiple image and text classification datasets, we
show that when regular ensembles suffer from overfit, our method eliminates the
harmful reduction in generalization due to overfit, and often even surpasses
the performance obtained by early stopping. Our method is easy to implement and
can be integrated with any training scheme and architecture, without additional
prior knowledge beyond the training set. It is thus a practical and useful tool
to overcome overfit. Code is available at
https://github.com/uristern123/United-We-Stand-Using-Epoch-wise-Agreement-of-Ensembles-to-Combat-Overfit.
