Summary of Exponential Moving Average Of Weights in Deep Learning: Dynamics and Benefits, by Daniel Morales-brotons et al.
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits
by Daniel Morales-Brotons, Thijs Vogels, Hadrien Hendrikx
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models. This paper presents a systematic study on the Exponential Moving Average (EMA) of weights, exploring its training dynamics and providing guidelines for hyperparameter tuning. EMA exhibits good early performance, partly explaining its success as a teacher model. Furthermore, EMA requires less learning rate decay compared to SGD since averaging reduces noise, introducing implicit regularization. The paper shows that EMA solutions differ from last-iterate solutions, generalizing better and exhibiting improved robustness to noisy labels, prediction consistency, calibration, and transfer learning. Therefore, the authors suggest using an EMA of weights as a simple yet effective plug-in to improve deep learning model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train artificial intelligence models called Exponential Moving Average (EMA). They studied how well EMA works and found it’s really good at making predictions. It’s also better at handling noisy data, which means it can make decisions even if the information isn’t perfect. The authors think this method could be useful for improving how AI models are trained. |
Keywords
» Artificial intelligence » Deep learning » Hyperparameter » Regularization » Stochastic gradient descent » Teacher model » Transfer learning
