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DK-CNNs: Dynamic kernel convolutional neural networks [PDF]

Neurocomputing, 2021
This paper introduces dynamic kernel convolutional neural networks (DK-CNNs), an enhanced type of CNN, by performing line-by-line scanning regular convolution to generate a latent dimension of kernel weights. The proposed DK-CNN applies regular convolution to the DK weights, which rely on a latent variable, and discretizes the space of the latent ...
Changjing Shang, Fei Chao, Chih-Min Lin   +2 more
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C-CNN: Contourlet Convolutional Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2021
Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditionally employed in the frequency domain.
Mengkun Liu, Licheng Jiao, Xu Liu 0006, Lingling Li 0002, Fang Liu 0001, Shuyuan Yang 0001   +5 more
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Convolutional Neural Networks (CNN)

2021
Convolutional neural networks (CNN or ConvNet) are a specific type of neural networks for processing grid-like data such as images and time series. In healthcare applications, the CNN models are widely used in automatic feature learning and disease classification from medical images, for example, automatic classification of skin lesions, detection of ...
Cao Xiao, Jimeng Sun
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Cellular neural network friendly convolutional neural networks — CNNs with CNNs

Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, 2017
This paper discusses the development and evaluation of a Cellular Neural Network (CeNN) friendly deep learning network for solving the MNIST digit recognition problem. Prior work has shown that CeNNs leveraging emerging technologies such as tunnel transistors can improve energy or EDP of CeNNs, while simultaneously offering richer/more complex ...
András Horváth, Michael Hillmer, Qiuwen Lou, Xiaobo Sharon Hu, Michael T. Niemier   +4 more
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Convolutional Neural Networks (CNN)

2022
Deep learning is one of the main technologies of machine learning. With deep learning, this chapter is talking about algorithms capable of mimicking the actions of the human brain through artificial neural networks. Compared to other algorithmic structures, neural networks have great advantages: first, their structure based on the stacking of non ...
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A Reversible-Logic based Architecture for Convolutional Neural Network (CNN)

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2021
Convolutional-Neural-Network (CNN) is a deep learning model, which is used extensively to solve complex image classification or computer vision problems. CNN and more complex architecture variants of it such as vggX, GoogleNet, ImageNet, etc. are widely used in various application domains such as object detection, self-driving cars, instance ...
Kasem Khalil, Bappaditya Dey, Ashok Kumar 0001, Magdy A. Bayoumi   +3 more
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Convolutional Neural Network (CNN): The architecture and applications

Applied Journal of Physical Science, 2022
The human brain is made up of several hundreds of billions of interconnected neurons that process information in parallel. Researchers in the field of artificial intelligence have successfully demonstrated a considerable level of intelligence on chips and this has been termed Neural Networks (NNs).
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Convolutional Neural Networks (CNN)

2019
Convolutional neural networks (CNN) are a specific type of neural network systems that are particularly suited for computer vision problems such as image recognition. In such tasks, the dataset is represented as a 2-D grid of pixels. See Figure 35-1.
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Convolutional Neural Networks (CNNs)

2017
This model’s development can be traced back to the 1950s, where researchers Hubel and Wiesel modeled the animal visual cortex. At length in a 1968 paper, they discussed their findings, which identified both simple cells and complex cells within the brains of the monkeys and cats they studied. The simple cells, they observed, had a maximized output with
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