[PDF] Top 20 Resiliency in Deep Convolutional Neural Networks
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Resiliency in Deep Convolutional Neural Networks
... of Convolutional Neural Networks increased the demand for larger GPU’s, to reduce this, smaller architectures like SqueezeNet [12] started taking the place of these larger architectures which were ... See full document
109
Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks
... It turns out that providing the network with good initialization parameters can have a significant impact on the accuracy of the trained model and capturing the long- term dependencies more efficiently. In this paper, we ... See full document
102
Disease Detection of Plants using Deep Learning and Convolutional Neural Networks
... CNN(Convolutional Neural Networks) through the help of Deep Learning ...Through Deep learning the accuracy in detecting an object gets ... See full document
5
Deep convolutional neural networks for Raman spectrum recognition:a unified solution
... Classification rates have been compared for various machine learning algorithms using Raman data. The method that is fre- quently reported to outperform other algorithms is support vec- tor machines (SVM) 8 . An SVM is ... See full document
8
Visualizing Resiliency Of Deep Convolutional Network Interpretations For Aerial Imagery
... the resiliency of a network after checking the class activation ...a deep CNN considers most important as it remembers from its training ...these networks allows us to visualize the variability of ... See full document
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Improving deep convolutional neural networks with unsupervised feature learning
... of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object clas- sification, achieving ... See full document
6
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
... We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough ... See full document
9
“Texting & Driving” Detection Using Deep Convolutional Neural Networks
... a deep learning–convolutional neural network (CNN) are implemented to detect such distracted ...V3 deep neural network, being trained to detect “texting and driving” ... See full document
16
Neonatal Seizure Detection Using Deep Convolutional Neural Networks
... artificial neural networks (ANNs) with several hidden lay- ...(not deep) artificial neu- ral networks used for seizure detection, 25–29 the deep networks do not need any ... See full document
20
Learning cartographic building generalization with deep convolutional neural networks
... reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have ...where deep learning approaches outperform traditional computer vision ...of ... See full document
20
Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition
... of convolutional layers is calculated automatically by subtracting the depth from the number of fully-connected ...of convolutional layers is controlled by the size of the input image, filter sizes, strides ... See full document
104
Clustering image sets with features from deep convolutional neural networks
... existing networks with learned weights on some domain ...pre-trained networks can learn general enough features to be used in transfer learning for other tasks [2, 37, ... See full document
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Retinal image quality assessment using deep convolutional neural networks
... In Deep Learning, instead of teaching a computer a massive list rules to solve the problem, it is given a model with which it can evaluate examples and a small set of instructions to modify the model when it makes ... See full document
182
Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks
... Conversion. Deep neural network architectures are giving ground breaking results in every field of artificial intelligence and, Natural Language Processing is one of ... See full document
10
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
... double-staged deep learning detection method is certainly ...non deep- learning methods the first stage of the method is the detection of objects in pictures to reduce the number of false positive of the ... See full document
105
SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks
... Up to now, a variety of SAR ATR methods have been proposed in the literature and these methods are generally implemented via two processing steps, i.e., feature extraction and classification. There are many algorithms ... See full document
9
Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... cial networks, biological networks, chemical networks, citation networks, and research networks, among ...graphs. Deep learning, which is a subfield of machine learning, has ... See full document
124
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
... As shown in Table 2, for every feature CNN-SVM outperforms the performance of the CNN. Following (Tsur et al., 2010), we have carried out a 5-fold cross-validation on this dataset. The baseline fea- tures (4.5) perform ... See full document
12
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
23
Hierarchical aesthetic quality assessment using deep convolutional neural networks
... Aesthetic image analysis has attracted much attention in recent years. However, assessing the aesthetic quality and assigning an aesthetic score are challenging problems. In this paper, we propose a novel framework for ... See full document
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