The double descent phenomenon presents significant implications for the effectiveness of Noisy Early Stopping (NES), particularly in low noise rate settings. In essence, double descent refers to the observation that as the number of training epochs increases, the clean test error may initially rise before subsequently decreasing, leading to a second peak in performance. This behavior complicates the application of NES, as it may lead to premature termination of training before the model reaches its optimal performance.
In low noise rate scenarios, where the model is more likely to overfit to the noise, the first peak in clean test accuracy may occur at a lower epoch count. If NES is implemented with a small patience parameter, it may stop training before the model can benefit from the second increase in accuracy. This could result in suboptimal model performance, as the model may not have had the opportunity to fully learn the underlying data distribution.
To mitigate the effects of double descent, practitioners should consider adjusting the patience parameter to allow for longer training periods, thereby accommodating the potential for a second peak in performance. Additionally, monitoring both noisy and clean validation accuracies could provide a more comprehensive view of model performance, enabling more informed decisions regarding when to halt training. Understanding the dynamics of double descent is crucial for optimizing NES and ensuring that it effectively captures the best model performance during training.