Titlebook: Generative Adversarial Learning: Architectures and Applications; Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Juergen Schmi Book 2022 The Editor(s

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书目名称Generative Adversarial Learning: Architectures and Applications读者反馈学科排名

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,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

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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

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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

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,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

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,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

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