An exponential moving average of weights consistently improves generalization, label-noise robustness, prediction consistency, calibration, and transfer learning for image classifiers.
There are many consistent explanations of unlabeled data: Why you should average
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Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits
An exponential moving average of weights consistently improves generalization, label-noise robustness, prediction consistency, calibration, and transfer learning for image classifiers.