Top 15 convolutional neural network architecture in 2022

Author: www.upgrad.com

Date Submitted: 06/04/2021 02:55 AM

Average star voting: 4 ⭐ ( 26337 reviews)

Summary: Did you know that you can classify or analyze images within a few lines of code using CNN? Check out this article that explains the Convolutional Neural Network architecture explaining its 5 layers.

Match with the search results: It has three layers namely, convolutional, pooling, and a fully connected layer. It is a class of neural networks and processes data having a ……. read more

Author: www.jeremyjordan.me

Date Submitted: 12/17/2021 10:51 AM

Average star voting: 4 ⭐ ( 58427 reviews)

Summary:

Match with the search results: In this post, I’ll discuss commonly used architectures for convolutional networks. As you’ll see, almost all CNN architectures follow the ……. read more

Author: towardsdatascience.com

Date Submitted: 02/12/2019 05:26 PM

Average star voting: 5 ⭐ ( 23987 reviews)

Summary: A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image. A digital image is a…

Match with the search results: A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) ……. read more

Author: towardsdatascience.com

Date Submitted: 04/06/2021 12:10 AM

Average star voting: 5 ⭐ ( 54512 reviews)

Summary: In this article, we will see what are convolutional neural network architecture and we will take basic CNN architecture as a case study.

Match with the search results: … overview of convolutional neural network architectures ……. read more

Author: www.analyticsvidhya.com

Date Submitted: 06/21/2021 01:05 PM

Average star voting: 3 ⭐ ( 80202 reviews)

Summary: Table Of Contents show What is CNN? Typical CNN Architecture LeNet Architecture AlexNet Architecture VGGNet Architecture Advantages of CNN Architecture…

Match with the search results: A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, ……. read more

Author: www.interviewbit.com

Date Submitted: 10/09/2020 02:42 AM

Average star voting: 3 ⭐ ( 47558 reviews)

Summary:

Match with the search results: In this article, we will see what are convolutional neural network architecture and we will take basic CNN architecture as a case study….. read more

Author: journalofbigdata.springeropen.com

Date Submitted: 01/08/2020 05:35 AM

Average star voting: 3 ⭐ ( 96890 reviews)

Summary: A convolutional neural network (CNN), is a network architecture for deep learning which learns directly from data. CNNs are particularly useful for finding patterns in images to recognize objects…

Match with the search results: Convolutional Neural Networks (CNN, or ConvNet) are a type of multi-layer neural network that is meant to discern visual patterns from pixel ……. read more

Author: www.v7labs.com

Date Submitted: 04/11/2020 06:43 PM

Average star voting: 5 ⭐ ( 96989 reviews)

Summary: AI, Data Science, Machine Learning, Deep Learning, Data Analytics, Python, R, Tutorials, News, Different types, CNN Architectures, Examples

Match with the search results: Convolutional Layer: In CNN architecture, the most significant component is the convolutional layer. It consists of a collection of ……. read more

Author: en.wikipedia.org

Date Submitted: 10/28/2019 01:39 PM

Average star voting: 5 ⭐ ( 64673 reviews)

Summary:

Match with the search results: Convolution neural network (also known as ConvNet or CNN) is a type of feed-forward neural network used in tasks like image analysis, natural ……. read more

Author: www.tutorialspoint.com

Date Submitted: 12/25/2019 09:50 AM

Average star voting: 4 ⭐ ( 90204 reviews)

Summary: Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional machine learning techniques in solving various real-life problems in computational intelligence fields, such as image classification. However, most existing CNN architectures were handcrafted from scratch and required significant amounts of problem domain knowledge from designers. A novel deep learning method abbreviated as TLBOCNN is proposed in this paper by leveraging the excellent global search ability of teaching–learning-based optimization (TLBO) to obtain an optimal design of network architecture for a CNN based on the given dataset with symmetrical distribution of each class of data samples. A variable-length encoding scheme is first introduced in TLBOCNN to represent each learner as a potential CNN architecture with different layer parameters. During the teacher phase, a new mainstream architecture computation scheme is designed to compute the mean parameter values of CNN architectures by considering the information encoded into the existing population members with variable lengths. The new mechanisms of determining the differences between two learners with variable lengths and updating their positions are also devised in both the teacher and learner phases to obtain new learners. Extensive simulation studies report that the proposed TLBOCNN achieves symmetrical performance in classifying the majority of MNIST-variant datasets, displays the highest accuracy, and produces CNN models with the lowest complexity levels compared to other state-of-the-art methods due to its promising search ability.

Match with the search results: CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution ……. read more

Author: medium.com

Date Submitted: 10/05/2020 01:26 AM

Average star voting: 4 ⭐ ( 97201 reviews)

Summary: This work examines convolutional neural networks (convnets) for image recognition, and provides an explanation for their architecture, and the role of various convnet hyperparameters will be examined. Since AlexNet was developed and applied to the ImageNet classi cation competition in 2012 [1], the quantity of research on convolutional networks for deep learning applications has increased remarkably. In 2015, the top 5 classi cation error was reduced to 3.57%, with Microsoft’s Residual Network [2]. The previous top 5 classi cation error was 6.67%, achieved by GoogLeNet [3]. In recent years, new arti cial neural network architectures have been developed which improve upon previous architectures. Speci cally, these are the inception modules in GoogLeNet, and residual networks, in Microsoft’s ResNet [2]. Here we will examine convolutional neural networks (convnets) for image recognition, and then provide an explanation for their architecture. The role of various convnet hyperparameters will be examined. The question of how to correctly size a neural network, in terms of the number of layers, and layer size, for example, will be considered. An example for determining GPU memory required for training a de ned network architecture is presented. The training method of backpropagation will be discussed in the context of past and recent developments which have improved training e ectiveness. Other techniques and considerations related to network training, such as choosing an activation function, and proper weight initialization, are discussed brie y. Finally, recent developments in convnet architecture are reviewed.

Match with the search results: It has three layers namely, convolutional, pooling, and a fully connected layer. It is a class of neural networks and processes data having a ……. read more

Author: vitalflux.com

Date Submitted: 04/05/2019 03:24 AM

Average star voting: 4 ⭐ ( 41763 reviews)

Summary:

Match with the search results: In this post, I’ll discuss commonly used architectures for convolutional networks. As you’ll see, almost all CNN architectures follow the ……. read more

Author: developer.ibm.com

Date Submitted: 09/28/2019 05:29 PM

Average star voting: 3 ⭐ ( 16862 reviews)

Summary: Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approac

Match with the search results: A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) ……. read more

Author: cs231n.github.io

Date Submitted: 11/08/2019 07:02 AM

Average star voting: 5 ⭐ ( 22266 reviews)

Summary: The memristor-based convolutional neural network gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition capability. Consequently, it is very suitable for building a wearable embedded application system and has broad application prospects in image classification, speech recognition and other fields. However, limited by the manufacturing process of memristive devices, high-precision weight devices are currently difficult to be applied in large-scale. In the same time, high-precision neuron activation function also further increases the complexity of network hardware implementation. In response to this, this paper proposes a configurable full-binary convolutional neural network (CFB-CNN) architecture, whose inputs, weights, and neurons are all binary values. The neurons are proportionally configured to two modes for different non-ideal situations. The architecture performance is verified based on the MNIST data set, and the influence of device yield and resistance fluctuations under different neuron configurations on network performance is also analyzed. The results show that the recognition accuracy of the 2-layer network is about 98.2%. When the yield rate is about 64% and the hidden neuron mode is configured as -1 and +1, namely ±1MD, the CFB-CNN architecture achieves about 91.28% recognition accuracy. Whereas the resistance variation is about 26% and the hidden neuron mode configuration is 0 and 1, namely 01MD, the CFB-CNN architecture gains about 93.43% recognition accuracy. Furthermore, memristors have been demonstrated as one of the most promising devices in neuromorphic computing for its synaptic plasticity. Therefore, the CFB-CNN architecture based on memristor is SNN-compatible, which is verified using the number of pulses to encode pixel values in this paper.

Match with the search results: … overview of convolutional neural network architectures ……. read more

Author: www.d2l.ai

Date Submitted: 09/02/2021 08:25 PM

Average star voting: 4 ⭐ ( 97914 reviews)

Summary: In this article, we explore concepts related to convolutional neural network architectures with the intention of building our understanding enough to create and understand the capabilities of an AlexNet model, from scratch.

Match with the search results: A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, ……. read more