Enlightenment Period Improving DNN Performance

In the early stage of deep neural network training, the loss decreases rapidly before gradually leveling off. Extensive research has shown that during this stage, the model parameters undergo significant changes and their distribution is largely established. Existing studies suggest that the introduction of noise during early training can degrade model performance. We identify a critical "enlightenment period" encompassing up to the first 4% of the training cycle (1--20 epochs for 500-epoch training schedules), a phase characterized by intense parameter fluctuations and heightened noise sensitivity. Our findings reveal that strategically reducing noise during this brief phase--by disabling data augmentation techniques such as Mixup or removing high-loss samples--leads to statistically significant improvements in model performance. This work opens new avenues for exploring the relationship between the enlightenment period and network training dynamics across diverse model architectures and tasks.