Add to ORKGAbstract. This paper proposes an algorithmic optimization for the fea-ture extractors of biologically inspired Convolutional Neural Networks (CNNs). CNNs are successfully used for different visual pattern recogni-tion applications such as OCR, face detection and object classification. These applications require complex networks exceeding 100,000 inter-connected computational nodes. To reduce the computational complex-ity a modified algorithm is proposed; real benchmarks show 65- 83% reduction, with equal or even better recognition accuracy. Exploiting the available parallelism in CNNs is essential to reduce the computa-tional scaling problems. Therefore the modified version of the algorithm is implemented and evaluated on a GPU platform to ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
In this paper, we propose an effective convolutional neural network (CNN) model to the problem of fa...
The focus of this paper is speeding up the evaluation of convolutional neural networks. While delive...
This paper proposes an algorithmic optimization for the feature extractors of biologically inspired ...
International audience—Deep Neural Networks are becoming the de-facto standard models for image unde...
Abstract. In this paper, we consider the problem of face detection un-der pose variations. Unlike ot...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
La prolifération des capteurs d'images dans de nombreux appareils électroniques, et l'évolution des ...
Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detectio...
Abstract: In recent years, Convolutional Neural Network (CNN) has been widely applied in speech/imag...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento d...
Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing an...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
In this paper, we propose an effective convolutional neural network (CNN) model to the problem of fa...
The focus of this paper is speeding up the evaluation of convolutional neural networks. While delive...
This paper proposes an algorithmic optimization for the feature extractors of biologically inspired ...
International audience—Deep Neural Networks are becoming the de-facto standard models for image unde...
Abstract. In this paper, we consider the problem of face detection un-der pose variations. Unlike ot...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
La prolifération des capteurs d'images dans de nombreux appareils électroniques, et l'évolution des ...
Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detectio...
Abstract: In recent years, Convolutional Neural Network (CNN) has been widely applied in speech/imag...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento d...
Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing an...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
In this paper, we propose an effective convolutional neural network (CNN) model to the problem of fa...
The focus of this paper is speeding up the evaluation of convolutional neural networks. While delive...