Early Stopping in Deep Networks: Double Descent and How to Eliminate it
This addresses the problem of optimizing training dynamics in deep learning for practitioners, offering a method to enhance early stopping by mitigating double descent, though it is incremental as it builds on existing understanding of bias-variance tradeoffs.
The paper tackles the epoch-wise double descent phenomenon in deep networks, showing it arises from a superposition of bias-variance tradeoffs in different network parts, and demonstrates that adjusting layer stepsizes to eliminate this can significantly improve early stopping performance, with empirical results on convolutional networks.
Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases at last. This intriguing double descent behavior also occurs as a function of training epochs and has been conjectured to arise because training epochs control the model complexity. In this paper, we show that such epoch-wise double descent arises for a different reason: It is caused by a superposition of two or more bias-variance tradeoffs that arise because different parts of the network are learned at different epochs, and eliminating this by proper scaling of stepsizes can significantly improve the early stopping performance. We show this analytically for i) linear regression, where differently scaled features give rise to a superposition of bias-variance tradeoffs, and for ii) a two-layer neural network, where the first and second layer each govern a bias-variance tradeoff. Inspired by this theory, we study two standard convolutional networks empirically and show that eliminating epoch-wise double descent through adjusting stepsizes of different layers improves the early stopping performance significantly.