Convolutional Neural Network
By Mário P. Véstias. 2020
969 citations recorded by the source corpus.
Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures
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Citations in the corpus
- Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks (2020, neutral): “The Convolutional Neural Network (CNN) [25] is adopted as the machine learning model for federated learning.”
- A survey of the recent architectures of deep convolutional neural networks (2019, background): “This approach finds the relations between words without taking into account any external source like parser or vocabulary (Kalchbrenner et al. 2014).”