Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation
PhotonicsWithin the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation.
Pei Hu +3 more
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ADAPTIVE MULTIMEDIA OPTIMIZATION USING GENERATIVE ADVERSARIAL NETWORKS AND ATTENTION-BASED DEEP FEATURE LEARNING FRAMEWORK [PDF]
ICTACT Journal on Image and Video ProcessingMultimedia systems have faced persistent challenges in maintaining perceptual quality under dynamic network and computational constraints. Traditional optimization techniques have struggled to preserve visual fidelity while adapting to heterogeneous ...
M. Subi Stalin, R. Prabakaran
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Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
Applied Sciences, 2021 A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional
Christine Dewi +3 more
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What are GANs?: Introducing Generative Adversarial Networks to Middle School Students
, 2021 Applications of Generative Machine Learning techniques such as Generative Adversarial Networks (GANs) are used to generate new instances of images, music, text, and videos.
Breazeal, Cynthia +2 more
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Generative Adversarial Networks in Brain Imaging: A Narrative Review
Journal of Imaging, 2022 Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases.
Maria Elena Laino +5 more
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Generative Adversarial Network for Medical Images (MI-GAN) [PDF]
Journal of Medical Systems, 2018 Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing ...
Talha Iqbal, Hazrat Ali
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Generative adversarial networks: project relevant overview [PDF]
, 2022 Generating synthetic data is a relevant point in the machine learning community. As accessible data is limited, the generation of synthetic data is a significant point in protecting patients' privacy and having more possibilities to train a model for ...
MartÃnez Madrid, Natividad +2 more
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IEEE AccessGenerative Adversarial Networks (GANs) have emerged as a powerful type of generative model, particularly effective at creating images from textual descriptions.
Patibandla Chanakya +2 more
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Generative Target Tracking Method with Improved Generative Adversarial Network
IET Circuits, Devices and Systems, 2023 Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation.
Yongping Yang, Hongshun Chen
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GANscan: continuous scanning microscopy using deep learning deblurring
Light: Science & Applications, 2022 In order to speed up the microscopy acquisition process, we developed a method, termed GANscan, in which videos are recorded as the stage is moving at high speeds.
Michael John Fanous, Gabriel Popescu
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