[PDF] Top 20 Deep Convolution Neural Networks for Automatic Eyeglasses Removal
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Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... with eyeglasses removal are shown in ...synthesized eyeglasses on face images have removed completely by the proposed ...99% eyeglasses on face images have been successfully removed using the ... See full document
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SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks
... Abstract. Deep convolutional neural networks (CNN) have recently proven extremely competitive in challenging visible light image and speech recognition ... See full document
9
Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation
... Recently, deep neural networks provided promising solu- tions for automatic image segmentation; however, they often perform good on regular samples ...difficulty-aware deep segmentation ... See full document
8
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...Various deep learning architectures such as deep neural ... See full document
5
Deep Sensing: Inertia and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks
... an automatic way of recognizing and classifying human activity ...for automatic recognition of human activity context is generally known as Human Activity Recognition (HAR) [13, ... See full document
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Survey on Unmanned Aerial Vehicle based Weeds Detection using Deep Neural Networks
... the automatic detection of weeds remains a challenging problem because of their strong similarity to the ...a deep learning approach, Convolutional Neural Networks (CNNs) with an unsupervised ... See full document
8
Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring
... in deep neural net- work models based on continuous-space represen- tation of the input and non-linear ...cently, deep learning techniques have been ap- plied to text analysis problems including AES ... See full document
10
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signal
... artificial neural network is used for ...of Deep Learning as a computational framework in various research fields, some papers have tried to use convolutional neural networks [19] to diagnose ... See full document
21
Vehicle Recognition based on Deep Convolution Neural Networks
... the automatic recognition of vehicle with the deep convolution neural network (DCNN) based on deep learning framework CAFFE and GPU which has the strong computing ...a deep ... See full document
7
Deep Belief Networks Using Convolution Neural Networks Algorithm
... of automatic speech recognizers , thus promising to extend their usability into the uman computer ...audio-visual automatic speech recognition and present novel contributions in two main areas: first, the ... See full document
8
A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... Convolutional Neural Networks (CNN) are gaining attention due to their capability to automatically discover relevant relative features in image classification ...words: Convolution layer, ... See full document
10
A Review on Indian Sign Language Recognition
... develop automatic Indian signing recognition ...Artificial Neural Networks (ANN), Support Vector Machine (SVM), Hidden Markov Models (HMM), Deep Convolution Neural ... See full document
13
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings
... The classification results can be further improved by using word representations that are obtained by concatenating the word vectors from different embeddings. We experimented with using all the 11 possible combinations ... See full document
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3D Firework Reconstruction from a Given Videos
... The Long Short-Term Memory Neural Network (LSTM), which is also a time-recursive neural network, is a special type of RNN. LSTM was proposed by Hochreiter & Schmidhuber in 1997 [10] and was improved and ... See full document
9
Image Description Using Deep Neural Network
... not effective. If you want to predict the next word in a sentence you have to know which words came before it. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output ... See full document
6
Phone recognition with hierarchical convolutional deep maxout networks
... convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech ...convolutional ... See full document
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ABSTRACT: Intrusion detection system plays a vital role in network security. Intrusion detection model may be a
... Signature-based options technique as a deep convolutional neural network [5] during a cloud platform is projected for plate localization, character detection and segmentation. Extracting vital options makes ... See full document
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TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding
... The comparison of operations between DNN and our SNN (L = 5, which is the one with highest accuracy) is shown in Table 2. Obviously, SNN reduces the multi- plication operations on all three benchmarks but also in- ... See full document
8
Machine Learning Perspectives for Dental Imaging
... The stochastic methodologies are used to identify the local information of the medical images, whether the image pixels are belongs to the desired region or not [11]. Methodologies like thresholding approaches, ... See full document
5
Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks
... uses Neural Networks/Deep Learning, one of the most effective and fastest-growing approaches in ...use neural networks for automatic coding of any sociophonetic ... See full document
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