[PDF] Top 20 Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
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Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
... towards an automated detection and localisation of key building defects, e.g., mould, deterioration, 18.. and stain, from images.[r] ... See full document
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Disease Detection of Plants using Deep Learning and Convolutional Neural Networks
... developed using CNN(Convolutional Neural Networks) through the help of Deep Learning ...Through Deep learning the accuracy in detecting an object gets ...in ... See full document
5
Individual minke whale recognition using deep learning convolutional neural networks
... The only known predictable aggregation of dwarf minke whales ( Balaenoptera acutorostrata subsp . ) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification ... See full document
12
Unified Framework For Deep Learning Based Text Classification
... Abstract: Deep learning has emerged as a very popular approach for solving large scale pattern recognition ...are deep learning based AI systems that have been trained to do sentiment analysis ... See full document
5
Research on Classification of Surface Defects of Hot rolled Steel Strip Based on Deep Learning
... semi-supervised learning method for classification of steel surface defects was proposed by ...on convolutional automatic encoder (CAE) and semi-supervised generation anti-network ...semi-supervised ... See full document
5
Deep Learning: Approaches and Challenges
... One of the most challenging problems of CNN is its training time, it takes days even weeks to train a model for a very huge dataset. Instead of training for all the data that are redundant and noisy, Liang et. al. [64] ... See full document
8
Convolutional Neural Networks and Hash Learning for Feature Extraction and of Fast Retrieval of Pulmonary Nodules
... hash- learning method, which learns binary Hash coding through deep learning technology for image retrieval, and has obtained a superior retrieval performance on public data ... See full document
16
An Algorithm for Power System Fault Analysis ...
... of using deep learning architecture using convolutional neural networks (CNN) for real-time power system fault ...harmonics using db4 Daubechies mother ... See full document
8
Understanding deep learning via backtracking and deconvolution
... Convolutional neural networks are widely adopted for solving problems in image clas- ...of deep learning through exploring the miss-classified cases in facial and emotion ...Keywords: ... See full document
14
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
... Deep learning (LeCun et al., 2015), and in particular CNN (LeCun et al., 1990, 1998; Krizhevsky et al., 2012), has gained a copious amount of attention in recent years as it has led to many state-of-the-art ... See full document
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Assessing the Corpus Size vs Similarity Trade off for Word Embeddings in Clinical NLP
... of deep learning methods in NLP has resulted in a significant num- ber of uses of embeddings to represent ...and deep learning models: these models excel with low-dimensional, continuous ... See full document
10
Deep Learning as a Frontier of Machine Learning: A Review
... through learning from the lower level by exploiting the hierarchical exploratory ...the deep learning methods avoid feature engineering in supervised learning ...unsupervised learning ... See full document
9
An Improved CNN Structure Model for Image Classification Recognition
... Ming Ye was born in China in 1974. He received the B.E, M.E and Ph.D degrees from University of Electronic Science and Technology, Chengdu, China, in 1996, 2005, and 2016, respectively. He joined Southwest University, ... See full document
8
Blind Navigation System using Artificial Intelligence
... Logits Layer, the final layer of our neural network is the logits layer, which will return the raw values for our predictions. The logit model is a regression model where the dependent variable (DV) is ... See full document
5
Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
... CNN has achieved state-of-the-art performance in a variety of applications, including natural language processing [28,29], speech recognition [30], and object recognition [31]. Inspired by the success of CNN in many ... See full document
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Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning
... proposed deep learning methods yield high performance in PASCAL VOC 2012 corpus, with the ...classification networks incorporating Alex, VGG, and GoogLe networks into a fully ...of ... See full document
21
Classification of Age and Gender using Deep Learning
... Deep learning is a class of machine learning algorithms that cause a precipitation of voluminous layers of nonlinear processing units for feature extraction and ...profound neural system (DNN) ... See full document
6
Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. As a 21.[r] ... See full document
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Human emotion recognition in video using subtraction pre-processing
... From table 4 it is clear that the overall AlexNet based structure and ResNet based structure show the best result, but GoogleNet has over 100 convolution layers, while AlexNet and ResNet-4 have only 12 convolution ... See full document
8
Deep convolutional neural networks capabilities for
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. We.[r] ... See full document
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