Summary of Repact: the Re-parameterizable Adaptive Activation Function, by Xian Wu et al.
RepAct: The Re-parameterizable Adaptive Activation Function
by Xian Wu, Qingchuan Tao, Shuang Wang
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper presents RepAct, a re-parameterizable adaptive activation function designed to optimize lightweight neural networks on edge devices. This innovative approach utilizes a multi-branch structure with learnable weights to enhance feature processing and cross-layer interpretability. Compared to conventional activation functions, RepAct achieves notable accuracy boosts in image classification and object detection tasks, such as up to 7.92% on MobileNetV3-Small for the ImageNet100 dataset, while maintaining computational complexity similar to HardSwish. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RepAct is a new way to make artificial intelligence work better on devices that can’t handle lots of computing power. It’s an activation function that helps train neural networks so they’re good at recognizing things like pictures and objects. The special thing about RepAct is that it’s very efficient, which means it uses less computer power than other methods. This makes it perfect for using in real-time applications, like controlling robots or self-driving cars. |
Keywords
» Artificial intelligence » Image classification » Object detection
