Understanding of a convolutional neural network

By Saad Albawi, Tareq Abed Mohammed, Saad Al-Zawi. International Conference on Emerging Technologies, 2017

3,297 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3a84214cb69ea0b34352285029f368b75718c32b

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Generative Adversarial Networks (2021, positive): “GAN S AND C ONVOLUTION N EURAL N ETWORKS Convolutional Neural Networks [15], or CNNs, are used as the generator and discriminator models in GANs, which generally deal with picture data.”
State-of-the-art in artificial neural network applications: A survey (2018, background): “Study have shown that CNN has an excellent performance in ML problems [35].”
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs (2022, background): “Since 2012, various CNN [20] algorithms are proposed.”
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images (2020, background): “If ‘N’ filters are used, then stacking those ‘N’ activation maps along the dept forms the full output of the convolutional layer [48] (see Figure 2).”
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, methodology): “A weightsharing method is used in the convolutional layers of CNN to reduce the total number of parameters [41,42].”
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray (2020, background): “The layers have a weight-sharing technique, which helps in reducing computation efforts [37] [38].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “There are different types such as Convolutional Neural Networks (CNNs) [54], Recurrent Neural Networks (RNNs) [55], and Multilayer Perceptrons (MLP) [56], which have already been applied to different themes.”
Convolutional Neural Networks: A Survey (2023, neutral): “Convolutional neural networks (CNNs) [14–16] are a popular type of ML model that have been widely utilized for various tasks, including image recognition [17–20], speech recognition [21–25], and NLP [26–30].”
Transformers in Time-Series Analysis: A Tutorial (2022, methodology): “Using multiple heads in a Transformer is analogous to using multiple kernels at each layer in a convolutional neural network, where each kernel is responsible for learning distinct features or representations [3].”
A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “Convolutional Neural Networks (CNNs) [18] are proposed for image classification with convolving filters that can extract features of pictures.”