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The infrequent occurrence of overfit in deep neural networks is perplexing.On the one hand, theory predicts that as models get larger they shouldeventually become too specialized for a specific training set, with ensuingdecrease in generalization. In contrast, empirical results in imageclassification indicate that increasing the training time of deep models orusing bigger models almost never hurts generalization. Is it because the way wemeasure overfit is too limited? Here, we introduce a novel score forquantifying overfit, which monitors the forgetting rate of deep models onvalidation data. Presumably, this score indicates that even whilegeneralization improves overall, there are certain regions of the data spacewhere it deteriorates. When thus measured, we show that overfit can occur withand without a decrease in validation accuracy, and may be more common thanpreviously appreciated. This observation may help to clarify the aforementionedconfusing picture. We use our observations to construct a new ensemble method,based solely on the training history of a single network, which providessignificant improvement in performance without any additional cost in trainingtime. An extensive empirical evaluation with modern deep models shows ourmethod's utility on multiple datasets, neural networks architectures andtraining schemes, both when training from scratch and when using pre-trainednetworks in transfer learning. Notably, our method outperforms comparablemethods while being easier to implement and use, and further improves theperformance of competitive networks on Imagenet by 1%.

Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs