Convolutional Neural Network learning representation
Learning text representation using recurrent convolutional neural network with highway layers
... 2.3 Convolutional Neural Networks In recent years, CNN has achieved great success in CV and has been proved to be effective in various NLP and IR ...
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Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks
... Furthermore, using only language-specific training data results in the need to man- ually annotate equivalently large amounts of training for each new language a sys- tem encounters. We propose a deep neu- ral ...
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Invariant descriptor learning using a Siamese convolutional neural network
... descriptor learning (Bengio et al., 2013). To test if machine learning approaches can achieve better results, Brown et ...descriptor learning framework, in which a descriptor is composed of four ...
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Understanding the Convolutional Neural Network & its Research Aspects in Deep Learning
... CONCLUSIONS Convolutional Neural Network (CNN) is the most important Deep Neural Network (DNN) architecture to implement the Deep Learning’s application of data and pattern ...
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Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
... by learning discriminative saliency map using Convolutional Neural Network ...the network as feature descrip- tors since they show excellent representation per- formance in ...
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Convolutional neural network extreme learning machine for effective classification of hyperspectral images
... To tackle this problem, a number of techniques have been proposed for feature extraction and dimensionality reduction (Yuan et al. 2017; Du et al. 2016, such as principal component analysis (PCA) (Zabalza et al. 2014), ...
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Traffic sign recognition using visual attribute learning and convolutional neural network
... The problem of extracting high level information from dig- ital images and videos is frequently faced in the area of com- puter vision and machine learning. For the recognition of traf- fic signs, a lot of ...
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Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm
... hierarchical learning, a wavelet transform has interesting features that make it a candidate to enhance the ...high-level representation by upward combinations of low-level representations such as pixels, ...
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Task-Driven Common Representation Learning via Bridge Neural Network
... deep learning based method, named bridge neural network (BNN) to dig the potential rela- tionship between two given data sources task by ...two convolutional neural net- works that ...
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Neural network based representation learning and modeling for speech and speaker recognition
... 1-D convolutional layer can be applied to the raw time-domain ...1-D convolutional filters can be thought of as finite impulse-response ...Stacked convolutional layers and pooling layers can ef- ...
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DNA Sequence Classification by Convolutional Neural Network
... as representation of sequences and Support Vector Machine as ...machine learning reposi- tory to show the performance of our model in dealing with general sequence ...
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Representation Learning With Convolutional Neural Networks
... ber representations need to be not only separable but also discriminative. Center loss was introduced which adds a cluster-based loss term to the cross-entropy loss to ensure the learned representations have both compact ...
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Convolutional neural network as an architecture for deep learning
... The convolutional layer have k filters of size n n q where n is smaller than the dimension of the image and q can either be the same as the number of channels r or smaller and may vary for each ...
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Learning Convolutional Neural Network For Face Verification
... Tracking-learning-detection (TLD) [32] is a method to tackle long-term object tracking in video. In TLD, starting from a single frame, the tracking procedure ex- changed information with an online learned detector ...
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Performance Comparison of Binarized Neural Network with Convolutional Neural Network
... Deep learning is a trending topic widely studied by researchers due to increase in the abundance of data and getting meaningful results with ...them. Convolutional Neural Networks (CNN) is one of the ...
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Glaucoma diagnosis using feature learning based on convolutional neural network
... feature learning technique for detecting glaucoma in coloured retinal fundus images using a deep learning ...on convolutional neural network (CNN) is developed to distinguish between ...
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Facial expression recognition using deep learning - convolutional neural network
... architecture convolutional neu- ral network achieves better accuracy than the state-of-the-art methods, the intensity nor- malization procedure and the neutral subtraction procedure achieves, respectively, ...
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Fixed layer Convolutional Neural Network
... This network has the structure explained in section II- C; more specifically it is composed by four 2D-convolution, four max-pooling, one flatten and one Dense with a soft-max activation ...of network was ...
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Convolutional Neural Network Language Models
... Abstract Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision ...language representation even in se- quential ...
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Convolutional Neural Network for Paraphrase Identification
... deep learning architecture Bi-CNN-MI for paraphrase identification ...using convolutional neural network (CNN) and model interaction features at each ...the network in a novel way using ...
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