Activation functions for convolutional neural networks: proposals and experimental study
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- Research areas:
- Year:
- 2023
- Type of Publication:
- Article
- Keywords:
- activation functions, convolutional networks, ELU
- Authors:
- Journal:
- IEEE Transactions on Neural Networks and Learning Systems
- Volume:
- 34
- Number:
- 3
- Pages:
- 1478-1488
- Month:
- Marzo
- ISSN:
- 2162-237X
- BibTex:
- Note:
- JCR(2023): 10.2 Position: 7/143 (Q1D1) Category: COMPUTER SCIENCE, THEORY & METHODS
- Abstract:
- Activation functions lie at the core of every neural network model, from shallow to deep convolutional neural networks. Their properties and characteristics shape the output range of each layer and, thus, their capabilities. Modern approaches rely mostly on a single function choice for the whole network, usually ReLU or other similar alternatives. In this work, we propose two new activation functions, analyse their properties and compare them with 17 different function proposals from recent literature on six distinct problems with different characteristics. The objective is to shed some light about their comparative performance. The results show that the proposed functions achieved better performance than the most commonly used ones.
- Comments:
- JCR(2023): 10.2 Position: 7/143 (Q1D1) Category: COMPUTER SCIENCE, THEORY & METHODS