標(biāo)題: Titlebook: Generative Adversarial Learning: Architectures and Applications; Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Juergen Schmi Book 2022 The Editor(s [打印本頁] 作者: 深謀遠(yuǎn)慮 時(shí)間: 2025-3-21 19:04
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書目名稱Generative Adversarial Learning: Architectures and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Generative Adversarial Learning: Architectures and Applications被引頻次
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書目名稱Generative Adversarial Learning: Architectures and Applications讀者反饋
書目名稱Generative Adversarial Learning: Architectures and Applications讀者反饋學(xué)科排名
作者: LAITY 時(shí)間: 2025-3-21 21:56 作者: nominal 時(shí)間: 2025-3-22 02:16
,Generative Adversarial Networks for?Data Augmentation in?Hyperspectral Image Classification,alistic hyperspectral data cubes that refrains from commonly used computationally intense model architectures. Dimensionality reduction is introduced as a preprocessing step that further reduces complexity while retaining only important information. The efficacy of the model is proven by verifying t作者: 殘忍 時(shí)間: 2025-3-22 04:45 作者: archenemy 時(shí)間: 2025-3-22 09:11
Inspection of Lead Frame Defects Using Deep CNN and Cycle-Consistent GAN-Based Defect Augmentation,hich makes it possible to translate normal patches on lead frame images to defect patches. The augmented defect patches are then blended into the lead frame images by using a linear blending method to obtain augmented lead frame images in training the faster R-CNN. Experimental results show that the作者: 寬大 時(shí)間: 2025-3-22 13:49
Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition,tablished and has led to 3 public machine learning challenges, which allows us to contrast our approach to the state of the art. Our GAN operates on 150 features extracted from 5s windows captured by a smartphone acceleration sensor carried at the hips. The most promising features are selected based作者: 寬大 時(shí)間: 2025-3-22 20:38
,Improved Diagnostic Performance of?Arrhythmia Classification Using Conditional GAN Augmented Heartb in heartbeats by augmenting specific class beats and improving the diagnostic performance of arrhythmia classification. A Convolution Neural Network based generator and discriminator is employed that incorporates the class information and conventional input for generating beats. Four publicly avail作者: 反抗者 時(shí)間: 2025-3-23 01:17
,Generative Adversarial Network Powered Fast Magnetic Resonance Imaging—Comparative Study and New Peing DNNs based on L1/L2 distance to the target fully sampled images could result in blurry reconstruction because L1/L2 loss can only enforce overall image or patch similarity and does not take into account local information such as anatomical sharpness. It is also hard to preserve fine image detail作者: 名次后綴 時(shí)間: 2025-3-23 05:12 作者: Affection 時(shí)間: 2025-3-23 07:39 作者: 適宜 時(shí)間: 2025-3-23 12:53
Pierre-Luc Pomerleau,David L. Loweryreformulation in (S1) leads to two novel empirical discriminator losses, termed the . (HVDL) and the . (SVDL) respectively, and a novel empirical generator loss. The error bounds of a discriminator trained with HVDL and SVDL are derived under mild assumptions. As case studies, experiments show that 作者: 預(yù)兆好 時(shí)間: 2025-3-23 15:05 作者: 是比賽 時(shí)間: 2025-3-23 20:29
Cyber Threats (and Opportunities),lead to improvements in biometric systems, assist in the search for missing people and in the identification of criminals in an automated way. In addition, generative models are currently used in multiple applications in entertainment. In the last decade, an increasing number of publications focused作者: 入伍儀式 時(shí)間: 2025-3-24 01:40
Artificial Intelligence and Data Mininghich makes it possible to translate normal patches on lead frame images to defect patches. The augmented defect patches are then blended into the lead frame images by using a linear blending method to obtain augmented lead frame images in training the faster R-CNN. Experimental results show that the作者: Amylase 時(shí)間: 2025-3-24 05:59
https://doi.org/10.1007/978-3-031-54184-1tablished and has led to 3 public machine learning challenges, which allows us to contrast our approach to the state of the art. Our GAN operates on 150 features extracted from 5s windows captured by a smartphone acceleration sensor carried at the hips. The most promising features are selected based作者: Accomplish 時(shí)間: 2025-3-24 07:20
,Parallel- und Rückkaufgesch?fte, in heartbeats by augmenting specific class beats and improving the diagnostic performance of arrhythmia classification. A Convolution Neural Network based generator and discriminator is employed that incorporates the class information and conventional input for generating beats. Four publicly avail作者: DECRY 時(shí)間: 2025-3-24 12:24 作者: 預(yù)示 時(shí)間: 2025-3-24 17:14 作者: 長矛 時(shí)間: 2025-3-24 19:58 作者: Accomplish 時(shí)間: 2025-3-24 23:47 作者: FEAS 時(shí)間: 2025-3-25 05:09 作者: albuminuria 時(shí)間: 2025-3-25 07:45
Fair Data Generation and Machine Learning Through Generative Adversarial Networks,e FairGAN framework can accommodate various fairness notions by changing the network architecture and objective functions of generators and discriminators. Under the FairGAN framework, we present three previously published model designs, Simplified-FairGAN [.], Causal-FairGAN [.], and FairGAN. [.], 作者: Anthem 時(shí)間: 2025-3-25 15:26
Quaternion Generative Adversarial Networks,ions of parameters requiring extensive computational capabilities. Building such huge models undermines their replicability and increases the training instability. Moreover, multi-channel data, such as images or audio, are usually processed by real-valued convolutional networks that flatten and conc作者: 頭腦冷靜 時(shí)間: 2025-3-25 18:45 作者: Perceive 時(shí)間: 2025-3-25 23:59 作者: 斜谷 時(shí)間: 2025-3-26 03:32 作者: 下垂 時(shí)間: 2025-3-26 04:56
Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Inteetection. This chapter studies a number of GAN architectures used for anomaly detection in the data stream. Moreover, a novel approach is proposed for embedding the dynamic characteristics of the data stream into the GAN-based detector structures. In this process, a GAN model is also proposed for ef作者: Blood-Clot 時(shí)間: 2025-3-26 08:55
Inspection of Lead Frame Defects Using Deep CNN and Cycle-Consistent GAN-Based Defect Augmentation,y. A lead frame is a thin layer of metal inside a chip package connecting a die to the circuitry on circuit boards. This chapter introduces the application of the faster region-based convolutional neural network (R-CNN) to detect and classify the defects on lead frames using AlexNet as a backbone. A作者: 浸軟 時(shí)間: 2025-3-26 13:59
Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition,ecognition of human activities from smartphone sensors, when limited training data is available. Generative Adversarial Networks (GANs) provide an approach to model the distribution of a dataset and can be used to augment data to reduce the amount of labelled data required to train accurate classifi作者: 社團(tuán) 時(shí)間: 2025-3-26 16:49
,GANs for?Molecule Generation in?Drug Design and?Discovery,rate novel molecules to build a virtual molecule library for further screening. With the rapid development of deep generative modeling techniques, researchers are now applying deep generative models, particularly Generative Adversarial Networks (GANs), for molecule generation. In this chapter, we tr作者: Headstrong 時(shí)間: 2025-3-27 00:41
,Improved Diagnostic Performance of?Arrhythmia Classification Using Conditional GAN Augmented Heartb an Electrocardiogram (ECG) signal helps in risk stratification, better medical assistance, and patient treatment. Due to privacy concerns, access to personal ECGs is restricted, hindering the development of automated computer-aided diagnosis systems. This chapter discusses an approach for generatin作者: 相符 時(shí)間: 2025-3-27 03:39 作者: Generic-Drug 時(shí)間: 2025-3-27 08:50
Generative Adversarial Networks for Data Augmentation in X-Ray Medical Imaging,ituations where little data or imbalanced datasets are present. There are two main reasons why some medical datasets are limited or imbalanced: either there is little data available for some rare diseases, or the privacy policy of medical organizations does not allow it to share the data. But deep l作者: faucet 時(shí)間: 2025-3-27 09:52 作者: Expressly 時(shí)間: 2025-3-27 16:22
,Generative Adversarial Networks: A?Survey on?Training, Variants, and?Applications,mage quality when GANs are used in image processing applications. The chapter reviews state-of-the-art GANs and focuses on the main advancements that involve adjusting the loss function, modifying the training process, and adding auxiliary neural network(s). A summary of different applications of GANs is also provided.作者: 仲裁者 時(shí)間: 2025-3-27 21:45 作者: 除草劑 時(shí)間: 2025-3-28 00:41
Counterterrorism and Cybersecurityselected GAN-based approaches in detecting malicious intrusions in an Internet of Things (IoT) network. Experiments are evaluated in terms of false alarm and missed alarm detection rates. The obtained results indicate the effectiveness of the proposed GAN-based detection approach for the respective task.作者: 十字架 時(shí)間: 2025-3-28 02:31 作者: 言行自由 時(shí)間: 2025-3-28 06:34
Enrico Bernardi,Silvia Romagnolihas shown that PGGAN generates good quality synthetic X-ray images for data augmentation to balance the dataset. The resulting balanced dataset used several classification models for testing. Various state-of-the-art classification models are adopted in transfer learning and fine-tuned to test the augmentation process.作者: IVORY 時(shí)間: 2025-3-28 12:20 作者: ANA 時(shí)間: 2025-3-28 15:38 作者: Concomitant 時(shí)間: 2025-3-28 22:04 作者: Thrombolysis 時(shí)間: 2025-3-29 01:22 作者: considerable 時(shí)間: 2025-3-29 06:43 作者: 財(cái)主 時(shí)間: 2025-3-29 08:22
Book 2022Ns as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great succe作者: 打火石 時(shí)間: 2025-3-29 14:28 作者: Iniquitous 時(shí)間: 2025-3-29 16:35 作者: 吞下 時(shí)間: 2025-3-29 22:44 作者: Budget 時(shí)間: 2025-3-30 03:52
Implementing Anti-counterfeiting Measuresions of parameters requiring extensive computational capabilities. Building such huge models undermines their replicability and increases the training instability. Moreover, multi-channel data, such as images or audio, are usually processed by real-valued convolutional networks that flatten and conc作者: 侵蝕 時(shí)間: 2025-3-30 07:52
Pierre-Luc Pomerleau,David L. Lowerying . (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioning on regression labels is mathematically distinct and raises two fundamental problems: (P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical vers作者: curettage 時(shí)間: 2025-3-30 10:04 作者: 簡略 時(shí)間: 2025-3-30 12:59
Cyber Threats (and Opportunities),d. Traditionally, there have been two kinds of modeling techniques used in this task: prototype-based and model-based methods. The first calculates the mean difference between age groups, and the latter uses parametric models to simulate change over time. Both approaches fail to keep individual char作者: ALB 時(shí)間: 2025-3-30 16:41 作者: Ataxia 時(shí)間: 2025-3-30 22:26
Artificial Intelligence and Data Miningy. A lead frame is a thin layer of metal inside a chip package connecting a die to the circuitry on circuit boards. This chapter introduces the application of the faster region-based convolutional neural network (R-CNN) to detect and classify the defects on lead frames using AlexNet as a backbone. A作者: 有惡意 時(shí)間: 2025-3-31 04:49
https://doi.org/10.1007/978-3-031-54184-1ecognition of human activities from smartphone sensors, when limited training data is available. Generative Adversarial Networks (GANs) provide an approach to model the distribution of a dataset and can be used to augment data to reduce the amount of labelled data required to train accurate classifi作者: flex336 時(shí)間: 2025-3-31 07:39
Chancen und Risiken privater Firmen,rate novel molecules to build a virtual molecule library for further screening. With the rapid development of deep generative modeling techniques, researchers are now applying deep generative models, particularly Generative Adversarial Networks (GANs), for molecule generation. In this chapter, we tr作者: 聰明 時(shí)間: 2025-3-31 09:27 作者: 開花期女 時(shí)間: 2025-3-31 16:15
https://doi.org/10.1007/978-3-030-37802-8ce of radiation, superior soft tissue contrast, and complementary multiple sequence information. However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical. Trad
