Summary of Swishrelu: a Unified Approach to Activation Functions For Enhanced Deep Neural Networks Performance, by Jamshaid Ul Rahman et al.
SwishReLU: A Unified Approach to Activation Functions for Enhanced Deep Neural Networks Performance
by Jamshaid Ul Rahman, Rubiqa Zulfiqar, Asad Khan, Nimra
First submitted to arxiv on: 11 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 proposes a novel activation function, SwishReLU, which combines elements of ReLU and Swish to address the issue of “Dying ReLU” in deep neural networks. Compared to traditional ReLU, SwishReLU offers better performance at a lower computational cost than Swish. The authors conduct an exhaustive comparison of different ReLU variants, including ELU, SeLU, Tanh, and SwishReLU on three datasets: CIFAR-10, CIFAR-100, and MNIST. Notably, applying SwishReLU in the VGG16 model yields a 6% accuracy improvement on the CIFAR-10 dataset. This research provides insights into the trade-offs between performance and computational cost for various activation functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making deep neural networks work better by combining two existing ideas, ReLU and Swish. Sometimes, these networks can get stuck in a bad state, called “Dying ReLU”. The authors propose a new way to combine the best of both worlds, called SwishReLU. They compare this new idea with other similar ones on three different datasets. Surprisingly, they found that using SwishReLU makes the network work 6% better on one dataset. |
Keywords
* Artificial intelligence * Relu * Tanh
