Evolving convolutional neural networks through grammatical evolution
By Ricardo H. R. Lima, Aurora Trinidad Ramirez Pozo. 2019
7 citations recorded by the source corpus.
The use of Convolutional Neural Networks (CNNs) have proven to be a solid approach often used to solve many Machine Learning problems, such as image classification and natural language processing tasks. The manual design of CNNs is a hard task, due to the high number of configurable parameters and possible configurations. Recent studies around the automatic design of CNNs have shown positive results. In this work, we propose to explore the design of CNN architectures through the use of Grammatical Evolution (GE), where a BNF grammar is used to define the CNN components and structural rules. Experiments were performed using the MNIST and CIFAR-10 datasets. The obtained results show that the presented approach achieved competitive results and maintaining relatively small architectures when compared with similar state-of-the-art approaches.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
Citations in the corpus
- Evolving Robust Neural Architectures to Defend from Adversarial Attacks (2019, background): “Currently, most of the current NAS suffer from high computational cost while searching in a relatively small search space [Lee et al., 2018; Lima and Pozo, 2019].”