deep convolutional neural network
Face Authentication Using Efficient Deep Convolutional Neural Network
... efficient deep Convolutional Neural network (CNN) is proposed to be used for face ...using deep convolutional neural ...efficient deep learning model is proposed to ...
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Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network
... Meanwhile, deep convolutional neural network (CNN) models have demon- strated extraordinary performance in medical image recognition tasks ...representative deep learning CNN ...
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Garments Texture Design Class Identification Using Deep Convolutional Neural Network
... Garments Texture Design Class Identification Using Deep Convolutional Neural Network.. S.M.[r] ...
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A Deep Convolutional Neural Network Based Lung Disorder Diagnosis
... Convolutional Neural Networks (CNNs) are very similar to normal Neural Networks (NNs). They are made up of neurons with their weights and biases. Each neuron receives inputs, performs a dot product ...
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Non Linear Text Regression with a Deep Convolutional Neural Network
... The network is trained with stochastic gradient de- scent and the Ada Delta (Zeiler, 2012) update rule using random restarts. Stochastic gradient descent is noisier than batch training due to a local esti- mation ...
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Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network
... Another inspiration of this work comes from the ob- servation that in the stack of convolutional layers within CNN, all the channels generated by the previous layer are treated equally for the next layer. This is ...
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Recognition of Brahmi words by Using Deep Convolutional Neural Network
... To minimize the over-fitting issue, this study trained the model in a controlled environment using a momentum value of 0.1 and it assisted to obtain the optimum performance. This study increased the number of ...
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Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... The paper presents the automatic eyeglasses removal based on deep convolutional neural network (DCNN). Our DCNN removes the eyeglasses region and reconstructs this region by selecting one ...
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Automated Detection of Gender from Face Images
... known as fine grained ethnicity. According to earlier results, the prediction of the fine-grained ethnicity of an individual is the most challenging task, followed by age and lastly gender. A. Dehghan et al. [7] ...
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Blind Navigation System using Artificial Intelligence
... artificial neural networks (SIANN), due to their shared-weight architecture and translation invariance ...The deep convolutional neural network can achieve reasonable performance on ...
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Fine Grained Classification of Product Images Based on Convolutional Neural Networks
... a deep convolutional neural network that the convolution kernel size and the order of network connection are based on the high efficiency of the filter capacity and cover- ...of ...
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A CNN Based Approach for Garments Texture Design Classification
... well-known deep Convolutional Neural Network (CNN) models AlexNet and VGG_S in two different garment datasets for the purpose of training and ...
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Automatic Plastic Waste Segregation And Sorting Using Deep Learning Model
... weakly-supervised deep learning approach (DCNN-GPC) for detection and recognition of nuclear waste ...on deep learning and also able to detect and categorize unknown waste ...of Deep ...
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Disease Detection in the Leaves of Multiple Plants
... The deep algorithms can be made useful in plant disease ...a deep learning based method for the detection of diseases effected in the leaves of ...the deep Convolutional Neural ...
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Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs
... as neural networks and regression-based techniques have also made great progresses ...the deep learning algorithms bloom, the CNN, LSTM, DBN and RNN modelling have become popular in some renewable energy ...
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Relation Classification via Convolutional Deep Neural Network
... The state-of-the-art methods used for relation classification are primarily based on statistical ma- chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features ...
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Facial Keypoints Detection with Deep Learning
... with deep convolutional neural ...deeper convolutional neural network such as the ResNet [6], and explore more image augmentation technique such as rotation and ...
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Deep Learning Based Visual Tracking: A Review
... Da Zhang, Hamid Maei, Xin Wang, and Yuan-Fang Wang presented the first neural-network tracker that combines convolutional and recurrent networks with RL algorithm in [12]. The tracker is capable of ...
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Three-dimensional convolutional restricted Boltzmann machine for human behavior recognition from RGB-D video
... three-dimensional convolutional restricted Boltzmann machine (3DCRBM) is proposed which can extract features from the raw RGB-D ...3D convolutional kernels can be applied to these four frames to extract ...
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MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval
... descriptors, deep learning models and ensemble ...In deep learning models, Multi-View CNN (MVCNN) (Su et ...and Deep Local feature Aggregation Network(DLAN) (Fu- ruya and Ohbuchi 2016) are ...
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