Summary of A Significantly Better Class Of Activation Functions Than Relu Like Activation Functions, by Mathew Mithra Noel et al.
A Significantly Better Class of Activation Functions Than ReLU Like Activation Functions
by Mathew Mithra Noel, Yug Oswal
First submitted to arxiv on: 7 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 paper introduces two novel activation functions, the Cone and Parabolic-Cone, which outperform traditional ReLU-like and Sigmoidal class activation functions on CIFAR-10 and Imagenette benchmarks. These cone activation functions exhibit unique properties, being positive only within a finite interval and strictly negative except at the end-points, where they become zero. This allows for more precise classification and the ability to learn complex patterns like the XOR function with fewer neurons. The Cone and Parabolic-Cone activation functions also boast larger derivatives than ReLU, leading to faster training times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces new activation functions that can help machines learn better. It shows that two new types of activation functions, called Cone and Parabolic-Cone, are better than the usual ones used for many tasks like image recognition. These new functions allow for more precise classification and can even learn complex patterns like the XOR function with fewer neurons. This could be important for many real-world problems where we need to separate different classes. |
Keywords
» Artificial intelligence » Classification » Relu
