Summary of Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function, by Hongye Zheng et al.
Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function
by Hongye Zheng, Bingxing Wang, Minheng Xiao, Honglin Qin, Zhizhong Wu, Lianghao Tan
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed research introduces two novel adaptive optimizers, sigSignGrad and tanhSignGrad, which integrate adaptive friction coefficients based on Sigmoid and Tanh functions respectively. These optimizers leverage short-term gradient information to enhance parameter updates, demonstrating improved optimization trajectory smoothness and convergence rate compared to existing methods. Theoretical analysis supports the effectiveness of the adaptive friction coefficient S, which aligns with targeted parameter update strategies. Experimental results on CIFAR-10, CIFAR-100, and Mini-ImageNet datasets using ResNet50 and ViT architectures confirm the superior performance of the proposed optimizers, showcasing improved accuracy and reduced training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces two new ways to help deep neural networks learn faster and better. The methods use a special type of friction that adjusts based on how quickly the network is changing. This helps the network avoid getting stuck in local optima or oscillating around a bad solution. The researchers tested their methods on several datasets and found that they performed better than existing methods, taking less time to train and achieving higher accuracy. |
Keywords
» Artificial intelligence » Optimization » Sigmoid » Tanh » Vit
