Summary of Adaptive Gradient Regularization: a Faster and Generalizable Optimization Technique For Deep Neural Networks, by Huixiu Jiang et al.
Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks
by Huixiu Jiang, Ling Yang, Yu Bao, Rutong Si, Sikun Yang
First submitted to arxiv on: 24 Jul 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 This paper introduces a novel optimization technique for deep neural networks called adaptive gradient regularization (AGR). By dynamically regulating gradients using sum normalization of gradient vectors as coefficients, AGR effectively controls optimization direction and improves training efficiency. This is achieved by smoothing the loss landscape, resulting in better model generalization performance. The proposed method can be applied to various vanilla optimizers, including Adan and AdamW, with only three lines of code added. Experimental results demonstrate improved training efficiency and enhanced model generalization performance on image generation, classification, and language representation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help deep learning models learn faster and better. Instead of using the same old methods, researchers developed a new technique called adaptive gradient regularization (AGR). AGR helps by adjusting how gradients are used to train the model, making it more efficient and accurate. This means that AGR can be used with many different types of optimizers, making it easy to use. The results show that AGR makes models learn faster and perform better on tasks like image recognition and language processing. |
Keywords
» Artificial intelligence » Classification » Deep learning » Generalization » Image generation » Optimization » Regularization
