Results 61 to 70 of about 5,285,158 (212)
AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper. The relevant networks can be called AAR-CNNs. AAR is the first method using the “abstraction extent” (predicted by AE
Yao Lu 0008 +3 more
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A robust deformed convolutional neural network (CNN) for image denoising
CAAI Transactions on Intelligence Technology, 2022 Abstract Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising.
Qi Zhang 0059 +4 more
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Mammal Sound Classification Using Convolution Neural Network [PDF]
, 2023 Recently, the acoustic signals topic has been emerging in every worldwide application. Based on these aspects, this paper continues to work on the development of the classification model for classifying marine mammal sounds using a convolution neural ...
Meka, Vaishnavi
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of a hyperspectral cube, feature extraction with 3-D convolution operation is a ...
Chunyan Yu +4 more
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CKFO: Convolution Kernel First Operated Algorithm with Applications in Memristor-based Convolutional Neural Network [PDF]
, 2023 IEEE This paper presents a new convolution algorithm: Convolution Kernel First Operated (CKFO), which can solve the problem that the actual calculation is not reduced after pruning the weight of the convolution neural network.
Huang, T +6 more
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System design and hardware realization of convolution neural network system in deep learning
Dianzi Jishu Yingyong, 2018 In this paper, a deep convolution neural network system is designed and implemented by FPGA hardware platform for the problem that the convolution neural network(CNN) in deep learning is slow and time consuming under the CPU platform. The system uses the
Wang Kun, Zhou Hua
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A New ECT Image Ｒeconstruction Algorithm Based on Convolutional Neural Network
Journal of Harbin University of Science and Technology, 2017 In response to the problem of image reconstruction in electrical capacitance tomography ( ECT) technology，the feasibility of applying convolutional neural network ( called CNN) to ECT image reconstruction is studied. On the basis of in-depth research for
LI Lan-ying, KONG Yin, CHEN De-yun
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Multi-level residual network VGGNet for fish species classification
Journal of King Saud University: Computer and Information Sciences, 2022 The development of an image-based fish classification system using Convolutional Neural Network (CNN) has the advantages of no longer directly conducting features extraction and several features analysis.
Eko Prasetyo +2 more
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, 2019 It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making
Shigemizu, Daichi +4 more
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Strategies in Jpeg Compression Using Convolutional Neural Network(Cnn) [PDF]
SSRN Electronic Journal, 2021 Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This
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