A survey of image synthesis and editing with generative adversarial networks

Wu, Xian; Xu, Kun; Hall, Peter M.

doi:10.23919/tst.2017.8195348

Cited by 152 publications

(82 citation statements)

References 4 publications

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“…The GANs (Generative Adversarial Nets) have been widely applied to the game area and achieved good results [29]. For future work we believe that investigating more sophisticated techniques for improving the accuracy of small object detection, including the Generative Adversarial Nets, will be beneficial.…”

Section: Discussionmentioning

confidence: 99%

Small Object Detection with Multiscale Features

Huang

et al. 2018

International Journal of Digital Multimedia Broadcasting

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The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. The detection models can get better results for big object. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. In this paper, we can achieve good detection accuracy by extracting the features at different convolution levels of the object and using the multiscale features to detect small objects. For our detection model, we extract the features of the image from their third, fourth, and 5th convolutions, respectively, and then these three scales features are concatenated into a one-dimensional vector. The vector is used to classify objects by classifiers and locate position information of objects by regression of bounding box. Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results.

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Section: Discussionmentioning

confidence: 99%

Small Object Detection with Multiscale Features

Huang

et al. 2018

International Journal of Digital Multimedia Broadcasting

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“…Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images [6] as shown in fig. 2.…”

Section: A Generative Adversarial Networkmentioning

confidence: 99%

From 2D Sketches to Photo-Realistic Images using Generative Adversarial Networks

Upadhyay*

2019

IJITEE

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With increasing technological advancements, there is a need for automation in this ever-evolving world. This may result in improved efficiency, faster work and enhanced capabilities. Sketch-to-image translation is an image processing application that can be used as a helping hand in a variety of fields. One of these is the utilization of Generative Adversarial Networks to guide edges to photographs, with the assistance of image generators and discriminators who work connected to produce realistic images. We have also incorporated Histogram of Oriented Gradients (HOG) as a feature/image descriptor. The HOG technique counts the gradient orientations to differentiate the target and the background. Support Vector Machine (SVM) is the classifier used for classification. This HOG and SVM model can be improved, altered and executed as multi-program software.

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“…Son yıllarda derin öğrenme teknikleri yapay öğrenme alanında bir çığır açmıştır [1]. Derin öğrenme, gerçek dünyadaki veri kümelerinde karmaşık yapıların öğrenildiği bir makine öğrenmesi yaklaşımı olarak ortaya çıkmıştır.…”

Section: Introductionunclassified

“…Büyük ölçekli veriler kullanılarak eğitilen derin sinir ağları, görüntülerin anlamsal olarak anlaşılmasıyla ilgili olarak klasik görüntü işleme tekniklerinden önemli ölçüde daha iyi performans göstermiştir [2]. Derin öğrenme, ayrıca görüntü üretiminde büyük bir yetenek sergilemiştir [1]. Derin ağların görüntü üretebilen ilk modeli 2014 yılında Goodfellow [3] tarafından önerilmiştir.…”

Section: Introductionunclassified

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Investigation of generative adversarial network models' image generation performance

Çelik

Talu

2020

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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One of the most important developments in the field of deep learning is the generative adversarial network(GAN) models. These models, known as GAN, are the most modern approaches used in image editing, image/cartoon painting, high resolution super image acquisition, and the transfer of the texture/pattern in another image to another image. In this study, the performances of GAN models (cGAN, DCGAN, InfoGAN, SGAN, ACGAN, WGAN-GP, LSGAN), which are commonly used in the literature, in producing synthetic images very similar to the real images were investigated. The originality of the study is the development of a hybrid GAN model (cDCGAN) that incorporates the advantages of cGAN and DCGAN and evaluates the performances of GAN methods in comparison with deep learning based convolutional neural networks (CNN). Synthetic images similar to the images in the data sets were generated with the encoded models. Fréchet inception distance (FID) metric and CNN were used to calculate the similarity of the produced synthetic images to the existing images so as to evaluate the model performance. In the experimental studies, time-based image production performances of all models were evaluated. As a result, it was observed that the images produced by the LSGAN model provide a high classification performance rate, but with DCGAN and WGANGP, it produces clearer noise images.

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A survey of image synthesis and editing with generative adversarial networks

Cited by 152 publications

References 4 publications

Small Object Detection with Multiscale Features

Small Object Detection with Multiscale Features

From 2D Sketches to Photo-Realistic Images using Generative Adversarial Networks

Investigation of generative adversarial network models' image generation performance

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