A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks

Wang, Lei; Chen, Wei; Yang, Wen‐Jia; Bi, Fangming; Yu, F. Richard

doi:10.1109/access.2020.2982224

Cited by 142 publications

(55 citation statements)

References 92 publications

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“…Recent approaches [9], [14], [31]- [33] of image-to-image translation use paired input-output examples to learn a mapping function applying a GAN based method. And more recent approaches [6], [8], [15]- [17] use the unpaired setting to learn a parametric translation function that does not need paired data samples.…”

Section: B Image-to-image Translationmentioning

confidence: 99%

Generative Adversarial Network for Multi Facial Attributes Translation

Liu

Feng

2021

IEEE Access

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Recently, Generative Adversarial Network (GAN) based approaches are applied in facial attribute translation. However, many tasks, i.e. multi facial attributes translation and background invariance, are not well handled in the literature. In this paper, we propose a novel GAN-based method that aims to get the target image that performs better within modifying one or more facial attributes in a single model. The model generator learns multi-points by inputting a re-coded transfer vector, ensuring the single model could learn multiple attributes simultaneously. It also optimizes the cycle loss to enhance the efficiency of transferring multi attributes. Moreover, the method uses the adaptive parameter to improve the calculation method of the loss function of the residual image. The results are also compared with the StarGAN v2, which is the current state-of-the-art model to prove the effectiveness and advancedness. Experiments show that our method has a satisfactory performance in multi facial attributes translation.

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Section: B Image-to-image Translationmentioning

confidence: 99%

Generative Adversarial Network for Multi Facial Attributes Translation

Liu

Feng

2021

IEEE Access

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“…Several articles [21,22,34,[47][48][49] have recently surveyed generative adversarial networks and GAN variants, including image synthesis as an application. Other surveys [50,51] covered image synthesis with GANs and partially discussed image-to-image translation. The effort closest to this survey is that of [50], where authors discussed image synthesis, including few image-to-image translation methods.…”

Section: Motivation and Contributionmentioning

confidence: 99%

“…Other surveys [50,51] covered image synthesis with GANs and partially discussed image-to-image translation. The effort closest to this survey is that of [50], where authors discussed image synthesis, including few image-to-image translation methods.…”

Section: Motivation and Contributionmentioning

confidence: 99%

Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

Alotaibi

2020

Symmetry

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Many image processing, computer graphics, and computer vision problems can be treated as image-to-image translation tasks. Such translation entails learning to map one visual representation of a given input to another representation. Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation, object transfiguration-related translation, etc. However, image-to-image translation techniques suffer from some problems, such as mode collapse, instability, and a lack of diversity. This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants. It also discusses and analyzes current state-of-the-art image-to-image translation techniques that are based on multimodal and multidomain representations. Finally, open issues and future research directions utilizing reinforcement learning and three-dimensional (3D) modal translation are summarized and discussed.

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“…Much research work regarding GANs has been done in designing architectures and training methods which leverage the quality of the generated images, as in StyleGAN2 [6], which uses a special style-based generator to produce high-resolution images. A comprehensive review of state-of-the-art GANs for various image processing tasks can be found in [8].…”

Section: Introductionmentioning

confidence: 99%

Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks

Steiniger

Kraus

Meisen

2021

JMSE

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The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, we use a GAN to augment a baseline sidescan image dataset with synthetic snippets. Although the training of a GAN with few data samples is likely to cause mode collapse, a combination of pre-training using simple simulated images and fine-tuning with real data reduces this problem. However, for sonar data, we show that this approach of transfer-learning a GAN is sensitive to the pre-training step, meaning that the vanishing of the gradients of the GAN’s discriminator becomes a critical problem. Here, we demonstrate how to overcome this problem, and thus how to apply transfer-learning to GANs for generating synthetic sidescan snippets in a more robust way. Additionally, in order to further investigate the GAN’s ability to augment a sidescan image dataset, the generated images are analyzed in the image and the frequency domain. The work helps other researchers in the field of sonar image processing to augment their dataset with additional synthetic samples.

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A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks

Cited by 142 publications

References 92 publications

Generative Adversarial Network for Multi Facial Attributes Translation

Generative Adversarial Network for Multi Facial Attributes Translation

Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks

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