Summary of Deep Learning Activation Functions: Fixed-shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-standard, Ensemble, by M. M. Hammad
Deep Learning Activation Functions: Fixed-Shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-Standard, Ensemble
by M. M. Hammad
First submitted to arxiv on: 14 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 The paper presents a comprehensive review of various types of activation functions (AFs) used in deep learning models, including fixed-shape, parametric, adaptive, stochastic/probabilistic, non-standard, and ensemble/combining types. The authors begin with a systematic taxonomy and classification frameworks that organize AFs based on their structural and functional distinctions. The review covers primary groups such as sigmoid-based, ReLU-based, and ELU-based AFs, discussing their theoretical foundations, mathematical formulations, and specific benefits and limitations in different contexts. The paper also explores miscellaneous AFs that do not conform to these categories but have shown unique advantages in specialized applications. Additionally, the authors examine strategies for combining multiple AFs to leverage complementary properties and provide a comparative evaluation of 12 state-of-the-art AFs using rigorous statistical and experimental methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at different types of activation functions used in deep learning models. It makes sense of all these different types and explains what they’re good for. The authors group them into categories like sigmoid-based, ReLU-based, and ELU-based. They also talk about some weird AFs that don’t fit into these categories but are useful in certain situations. The paper even shows how to use multiple AFs together to get better results. |
Keywords
* Artificial intelligence * Classification * Deep learning * Relu * Sigmoid
