Summary of Activation Function Optimization Scheme For Image Classification, by Abdur Rahman et al.
Activation Function Optimization Scheme for Image Classification
by Abdur Rahman, Lu He, Haifeng Wang
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes an evolutionary approach to optimize activation functions specifically for image classification tasks, aiming to discover high-performing alternatives to existing state-of-the-art options. The proposed method, called Exponential Error Linear Unit (EELU), is evaluated using five neural network architectures and eight standard datasets, including CIFAR10, Imagenette, MNIST, Fashion MNIST, Beans, Colorectal Histology, CottonWeedID15, and TinyImageNet. Results show that the optimized activation functions outperform existing ones in 92.8% of cases, with -x∗erf(e^(-x)) being found to be the best activation function for image classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a better way to make deep neural networks work well. It’s like searching for the perfect recipe for making delicious cookies! The team used an “evolutionary” approach to find new and improved ways of making neurons in the network work together. They tested their new method on lots of different types of images, and it worked really well – better than some other methods that are already out there. |
Keywords
» Artificial intelligence » Image classification » Neural network
