TSEV-GAN: Generative Adversarial Networks with Target-aware Style Encoding and Verification for facial makeup transfer

Xu, Zhen Bo; Wu, Si; Jiao, Qianfen; Wong, Hau-San

doi:10.1016/j.knosys.2022.109958

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…The discriminator plays a crucial role in distinguishing between real data from the training dataset and synthetic data generated by the generative network. Pre-trained discriminators can reduce the number of epochs required for training the entire GAN system so that the desired training results will be potentially faster and better [23].…”

Section: Image Generationmentioning

confidence: 99%

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Color Face Image Generation with Improved Generative Adversarial Networks

Chang,

Chung,

Chai

et al. 2024

Electronics

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This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color.

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Section: Image Generationmentioning

confidence: 99%

“…Image-to-image models focus on transforming one type of image into another, while text-to-image applications specifically involve the generation of images from textual descriptions. These concepts showcase the versatility of generative models in learning complex mappings and generating diverse types of data [20][21][22][23].…”

Section: Introductionmentioning

confidence: 96%

Color Face Image Generation with Improved Generative Adversarial Networks

Chang,

Chung,

Chai

et al. 2024

Electronics

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“…By engaging in a game between the two, the final generated data are made to be indistinguishable from reality [18]. This method has found broad applications [19], such as data augmentation [20,21], image style transfer [22,23], image super-resolution [24,25], and text-to-image generation. GAN adopts an unsupervised learning approach, automatically learning from the source data to produce astonishing results without the need for manual labeling of the dataset [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Sample Expansion and Classification Model of Maize Leaf Diseases Based on the Self-Attention CycleGAN

Guo,

Li,

Hou

et al. 2023

Sustainability

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In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over 91%. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks.

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