Summary of Activation Function Optimization Method: Learnable Series Linear Units (lslus), by Chuan Feng et al.
Activation function optimization method: Learnable series linear units (LSLUs)
by Chuan Feng, Xi Lin, Shiping Zhu, Hongkang Shi, Maojie Tang, Hua Huang
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 research proposes a novel activation function, Learnable Series Linear Units (LSLU), which adapts to the training stage and improves deep learning network accuracy. LSLU simplifies networks while enhancing non-linearity by introducing learnable parameters θ and ω that control the activation function. The method is evaluated on CIFAR10, CIFAR100, and specific task datasets, showing a 3.17% accuracy improvement on CIFAR100 for VanillaNet. The convergence behavior of θ and ω is analyzed, revealing an initial decrease followed by an increase in θ and an opposite trend for ω. LSLU enhances generalization ability while speeding up training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve artificial neural networks, called Learnable Series Linear Units (LSLU). It makes the network more flexible and better at recognizing patterns. The authors tested this idea on different datasets and found that it can make the network perform 3.17% better than before. They also studied how the method works and what it does to improve the network’s performance. |
Keywords
» Artificial intelligence » Deep learning » Generalization
