Generative Adversarial Networks : A Survey

kakkar, Tanuj; Singh, Upasna

doi:10.21203/rs.3.rs-300223/v1

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Methods based on GANs have been applied to generate high-quality COVID-19 CT images [35,36]. However, GANs usually require enormous data with high computational costs, especially when high-quality and high-resolution synthetic images are needed [37,38]. The time required to customise and fine tune the model is ill-advised for rapid response in a fast-spreading pandemic.…”

Section: Synthetic Ct Images 221 Selection Of Synthesis Methodsmentioning

confidence: 99%

A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak

Hou

Navarro‐Cía

2023

Biomed. Phys. Eng. Express

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Coronavirus disease 2019 (COVID-19) has spread globally for over three years, and chest computed tomography (CT) has been used to diagnose COVID-19 and identify lung damage in COVID-19 patients. Given its widespread, CT will remain a common diagnostic tool in future pandemics, but its effectiveness at the beginning of any pandemic will depend strongly on the ability to classify CT scans quickly and correctly when only limited resources are available, as it will happen inevitably again in future pandemics. Here, we resort into the transfer learning procedure and limited hyperparameters to use as few computing resources as possible for COVID-19 CT images classification. Advanced Normalisation Tools (ANTs) are used to synthesise images as augmented/independent data and trained on EfficientNet to investigate the effect of synthetic images. On the COVID-CT dataset, classification accuracy increases from 91.15% to 95.50% and Area Under the Receiver Operating Characteristic (AUC) from 96.40% to 98.54%. We also customise a small dataset to simulate data collected in the early stages of the outbreak and report an improvement in accuracy from 85.95% to 94.32% and AUC from 93.21% to 98.61%. This study provides a feasible Low-Threshold, Easy-To-Deploy and Ready-To-Use solution with a relatively low computational cost for medical image classification at an early stage of an outbreak in which scarce data are available and traditional data augmentation may fail. Hence, it would be most suitable for low-resource settings.

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Section: Synthetic Ct Images 221 Selection Of Synthesis Methodsmentioning

confidence: 99%

A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak

Hou

Navarro‐Cía

2023

Biomed. Phys. Eng. Express

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“…GAN does not need a Markov chain, only uses back propagation to obtain gradient. GAN does not need reasoning in the learning process, and a variety of functions can be integrated into the model [20]. GAN cannot learn because the generator begins to degenerate, whose learning process is difficult, and the schemes oscillate, or generator tends to collapse [21].…”

Section: Advantages and Disadvantages Of Ganmentioning

confidence: 99%

Generative Adversarial Network and Its Application in Energy Internet

Xiao

2022

Mathematical Problems in Engineering

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Energy Internet (EI) can provide consumers with flexible energy-sharing services. Recently, artificial intelligence (AI) technology has been widely used in the field of EI. Generative adversarial network (GAN) is one of the hottest research directions in the field of AI in recent years, and its excellent data generation ability has attracted wide attention. First, this paper introduces the framework, advantages, disadvantages, and improvement of classic GAN. Then, the application of GAN in the field of EI is reviewed. Finally, the paper is summarized, and the possible application of GAN in EI is prospected.

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A lightweight IoT firmware vulnerability detection scheme based on homology detection

Liu¹,

Zhang²,

Wang³

et al. 2022

JHS

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In recent years, with the rapid development of IoT technology, hundreds of millions of IoT devices have been manufactured and applied, and the subsequent IoT attacks have become more and more severe. The complex and diverse architecture of IoT devices, coupled with the lack of security development specifications by IoT device manufacturers, and the widespread misuse and abuse of code, lead to the proliferation of IoT vulnerabilities. The conventional IoT vulnerability detection scheme is expensive to operate, and the implementation technology is complex, which is difficult to be fully promoted. This paper proposes a lightweight IoT firmware vulnerability detection scheme based on homology detection. The processing is converted into a feature vector, which effectively reduces the platform dependence. Combined with the database technology, the storage and retrieval efficiency is increased, and the same-origin vulnerability detection is realized by calculating the cosine similarity of the vector. The experimental results show that this scheme can effectively identify the vulnerabilities in firmware.

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Generative Adversarial Networks : A Survey

Cited by 3 publications

References 29 publications

A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak

A computationally-inexpensive strategy in CT image data augmentation for robust deep learning classification in the early stages of an outbreak

Generative Adversarial Network and Its Application in Energy Internet

A lightweight IoT firmware vulnerability detection scheme based on homology detection

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