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

TL;DR

This paper introduces a new overfit measure based on forgetting rates, revealing that overfit can occur without accuracy drops, and proposes a training-history-based ensemble method that improves deep model performance efficiently.

Contribution

The paper presents a novel overfit score and a training-history-based ensemble method that enhances deep neural network performance without additional training cost.

Findings

Overfit can occur without validation accuracy decrease.

The proposed ensemble method improves performance across datasets.

Method outperforms comparable approaches and boosts Imagenet accuracy by 1%.

Abstract

The infrequent occurrence of overfit in deep neural networks is perplexing. On the one hand, theory predicts that as models get larger they should eventually become too specialized for a specific training set, with ensuing decrease in generalization. In contrast, empirical results in image classification indicate that increasing the training time of deep models or using bigger models almost never hurts generalization. Is it because the way we measure overfit is too limited? Here, we introduce a novel score for quantifying overfit, which monitors the forgetting rate of deep models on validation data. Presumably, this score indicates that even while generalization improves overall, there are certain regions of the data space where it deteriorates. When thus measured, we show that overfit can occur with and without a decrease in validation accuracy, and may be more common than previously appreciated. This observation may help to clarify the aforementioned confusing picture. We use our observations to construct a new ensemble method, based solely on the training history of a single network, which provides significant improvement in performance without any additional cost in training time. An extensive empirical evaluation with modern deep models shows our method's utility on multiple datasets, neural networks architectures and training schemes, both when training from scratch and when using pre-trained networks in transfer learning. Notably, our method outperforms comparable methods while being easier to implement and use, and further improves the performance of competitive networks on Imagenet by 1%.