[PDF] Top 20 Active Generative Adversarial Network for Image Classification

Active Generative Adversarial Network for Image Classification

... Recently, the GAN models have been used with transfer learning (Choe et al. 2017; Bousmalis et al. 2017), zero- shot learning (Tong et al. 2018; Wang et al. 2018), semi- supervised learning (Dai et al. 2017; Lee et al. ... See full document

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Semi-supervised generative adversarial nets with multiple generators for SAR image recognition

... The adversarial game between the discriminator and the generator enable the discriminator to have a better feature extraction capability than RBM et ...semi-supervised classification on MNIST (Modified ... See full document

19

AlphaGAN: Generative adversarial networks for natural image matting

... first generative adversarial network (GAN) for natural image mat- ...generator network is trained to predict visually appealing alphas with the addition of the adversarial loss ... See full document

17

Improving Image Captioning with Conditional Generative Adversarial Nets

... erative adversarial nets and reinforcement learning to im- prove existing image captioning ...based image captioning algorithms directly optimize lan- guage evaluation metrics such as CIDEr, BELU and ... See full document

9

Retinal image synthesis from multiple-landmarks input with generative adversarial networks

... as image classification [1], detection [2], and segmentation ...medical image analysis, researchers need to collect large amounts of training ...medical image datasets and shortage of good ... See full document

15

Retinal Image Synthesis and Semi-supervised Learning for Glaucoma Assessment.

... volutional Generative Adversarial Network (DCGAN) [10]: an image synthesizer and a semi-supervised learning ...vessel network and optic ... See full document

10

Realistic Hair Simulator for Skin lesion Images Using Conditional Generative Adversarial Network

... These simulation methods were useful at the early stages for validation of the proposed methods and overcome the shortage in the annotated testing images [17]. Nevertheless, they based their simulation techniques on ... See full document

11

Generating 3D Models Using 3D Generative Adversarial Network

... 3D shape awareness is an old problem in Computer vision which is not yet completely solved by the community. A large amount of work focus on 3D reconstruction. The main goal of the computer vision research is to figure ... See full document

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Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets

... In all the evaluations, we follow (Gulrajani et al. 2017) to use residual network for all compared models. The set- ting of the employed residual networks is the same as the ones used in (Gulrajani et al. 2017). ... See full document

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Research on Image Translation and Image Quality Evaluation Based on Generative Adversarial Networks

... of image translation is an important research ...on image translation quality are studied experimentally, and the model parameters and algorithm optimization methods are ...of image translation ... See full document

6

Enhancing neural non-intrusive load monitoring with generative adversarial networks

... a Generative Adversarial Network to mitigate the described ...with Generative Adversarial Networks show the potential of the approach, albeit there is no strong evidence yet that this ... See full document

8

Hippocampal subfields segmentation in brain MR images using generative adversarial networks

... including image segmentation, classification, and ...the classification of MRI images in the hippocampus of AD, MCI, and nor- mal brain ...deep network, to achieve the pixel-wise ... See full document

12

RETRACTED: Realization of Virtual Human Face Based on Deep Convolutional Generative Adversarial Networks

... Processing generative adversarial networks and used them for LSUN scene recognition challenges, Mnist handwritten numbers, and SVNH data sets ...scene image is successfully ...convolutional ... See full document

12

CBNWI 50: A Deep Learning Bird Dataset for Image Translation and Resolution Improvement using Generative Adversarial Network

... Abstract: Generative Adversarial Networks have gained prominence in a short span of time as they can synthesize images from latent noise by minimizing the adversarial cost ...like image ... See full document

12

A Sensor Image Super-Resolution via Advanced Generative Adversarial Network

... and Generative Adversarial Networks (GANs) have given birth comparing with state-of-the-art algorithms on image ...the generative model, which tries to produce counterfeit without detection, ... See full document

18

Yet Another Text Captcha Solver:A Generative Adversarial Network Based Approach

... This paper presents a generic, low-effort yet effective approach to automatically solve text-based captchas based on deep learning [31, 48]. Unlike previous machine-learning-based attacks [55] that all require a large ... See full document

17

GANGs: Generative Adversarial Network Games

... data, green points are generated data. Blue indicates areas that are classified as ‘realistic’ while red indicates a ‘fake’ classification by C . Plain application of PNM. In GANGs, G informs C about what good ... See full document

9

Compressed Sensing MRI Reconstruction Based on Generative Adversarial Nets

... as image recognition [13], classification [14], super resolution [15, 16] and denoising ...The network learned the mapping between zero-filled and full-sampled k-space ...residual network is ... See full document

9

Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X

... Keywords: dialectical generative adversarial network; image translation; Sentinel-1; TerraSAR-X.. Introduction 28.[r] ... See full document

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Vol 6, No 4 (2020)

... a generative adversarial network (GAN) model that is trained with human eye fixations to predict saliency, and the depth information provided by an RGB-D ... See full document

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