[PDF] Top 20 A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images
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A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images
... years, deep learning has achieved great successes in the field of object recognition in ...the networks and to learn the target features, which avoid complex preprocessing and can also achieve better ... See full document
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Generative Adversarial Networks: A Comparative Analysis
... conditional adversarial nets are able to perform the above mentioned tasks efficiently because the nets not only learn the mappings between inputs and outputs, but also learn the loss function used to train the ... See full document
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Denoising of Images using Deep Convolutional Neural Networks (DCNN)
... the convolutional networks do not produce any clearly useful ...natural images before training, other than the localized receptive field structure of ... See full document
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GANGs: Generative Adversarial Network Games
... Generative Adversarial Networks (GANs) [14] are a framework in which two neural networks compete with each other: the generator (G) tries to trick the classifier (C) into classifying its ... See full document
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Hippocampal subfields segmentation in brain MR images using generative adversarial networks
... traditional convolutional neural network, the generative adver- sarial network attempts to fit the distribution of the generated segmentation results to the one of real ...The adversarial training ... See full document
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Wasserstein Generative Adversarial Privacy Networks
... We assume that we do not know what the distribution H exactly looks like, but we are able to sample points from H, for example by scraping images off the internet or by taking photos of humans. We then want to try ... See full document
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Generating input data for microstructure modelling: A deep learning approach using generative adversarial networks
... Neural networks require hyperparameters to be fully functional. These are a set of parameters that are defined for the NN, before the training starts. The most important ones are the width and depth of the ... See full document
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A Study of Generative Adversarial Networks in 3D Modelling
... The adversarial modeling is a framework for generating objects, which has a deep impact on the development of generation ...uses adversarial process estimation for generating ...the generative ... See full document
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Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields
... remotely-sensed images: aerial (or very high resolution, VHS) images and satellite (or high resolution, HR) images, has been applied to many application domains, especially road extraction in which ... See full document
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RETRACTED: Realization of Virtual Human Face Based on Deep Convolutional Generative Adversarial Networks
... DOI: 10.4236/jsip.2018.93013 224 Journal of Signal and Information Processing In this paper, the experiment activation function uses the sigmoid function to calculate the loss value generated during the training process ... See full document
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Compression artifacts reduction by improved generative adversarial networks
... on generative adversarial networks (GANs) ...an adversarial process alternating between identifying and faking, and the generative ad- versarial losses are formulated to evaluate the ... See full document
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PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification
... Most methods today for ECG classification focus on applying classical supervised machine learning methods (Chazal and Reilly 2007; Ye, Kumar, and Coimbra 2012; Escalona-Moran et al. 2014) and manual feature engineer- ... See full document
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Beyond Local Nash Equilibria for Adversarial Networks
... better generative models and are po- tentially closer to true Nash equilibria than those found by GANs ...like images: image quality and log likelihood are not aligned as for instance shown by Theis et ... See full document
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Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... Resolution Convolutional Neural Network (SRCNN) proposed by Dong [6] shows the great potential of an end-to-end DCN in image super- ...high-resolution images, with little pre/post-processing beyond the ... See full document
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Age Estimation Method Based on Generative Adversarial Networks
... face images include face detection, identity authentication, and face characters (gender, age, expression and race) which have become research hot issues in the field of computer vision and man- machine ... See full document
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Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks
... amount of those lesions have ensued in brutal NPDR, and retinal blood deliver progresses progressively more negotiation [13]. As a corollary, the non-perfused parts of the retina direct signals inspiring fresh blood ... See full document
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Generative Adversarial Networks for Text Using Word2vec Intermediaries
... All the significant advances in the adaptation of GANs since its introduction in 2016, has been fo- cused in the field of images. We have got to the point, where sometimes GAN architectures have managed to ... See full document
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Generating 3D Models Using 3D Generative Adversarial Network
... 2.3 Deep learning on 3D data : The computer vision community has seen a rapid improvement of neural networks in various task. In the task of 3D model recognition Li et al. [2015], Su et al. [2015b], Girdhar ... See full document
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AlphaGAN: Generative adversarial networks for natural image matting
... first generative adversarial network (GAN) for natural image mat- ...the adversarial loss from the discriminator that is trained to classify well- composited ...in convolutional neural ... See full document
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Generative Adversarial Networks: Outline and its Use Cases
... 1) DCGAN: DCGANs were the initial main development on the GAN architecture. They are more steady in terms of generating higher quality samples andz training.[9] The writers of the DCGAN engrossed themselves on improving ... See full document
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