XGrad: Boosting Gradient-Based Optimizers With Weight Prediction.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Gradient-based optimizers are fundamental to training deep neural networks (DNNs).
• Existing optimizers face challenges in convergence speed and generalization performance.
• Improving DNN training efficiency and accuracy remains a key research area.

Purpose of the Study:

• To introduce XGrad, a novel deep learning training framework.
• To enhance gradient-based optimizers by incorporating weight prediction.
• To improve the convergence and generalization of DNN models.

Main Methods:

• XGrad integrates future weight prediction into standard gradient-based optimizers.
• Future weights are predicted before each mini-batch using the optimizer's update rule.
• Predicted future weights guide both forward and backward passes during training.

Main Results:

• XGrad significantly boosts the convergence of popular gradient-based optimizers.
• The framework improves the generalization capabilities of DNN models.
• Empirical results show higher model accuracy compared to baseline optimizers across five methods (SGD with momentum, Adam, AdamW, AdaBelief, AdaM3).

Conclusions:

• XGrad offers a straightforward yet effective method for enhancing DNN training.
• Weight prediction is a valuable technique for improving optimizer performance.
• The proposed framework demonstrates broad applicability and effectiveness across various optimizers.