相较原始的GAN,DCGAN几乎完全使用了卷积层代替全链接层,判别器几乎是和生成器对称的,整个网络没有pooling层和上采样层的存在,实际上是使用了带步长(fractional-strided)的卷积代替了上采样,以增加训练的稳定性

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论文题目

基于CGAN的人脸深度图估计: Face Depth Estimation With Conditional Generative Adversarial Networks

论文摘要

深度图的估计和单幅或多幅人脸图像的三维重建是计算机视觉的一个重要研究领域。在过去十年中提出和发展了许多方法。然而,像健壮性这样的问题仍然需要通过进一步的研究来解决。随着GPU计算方法的出现,卷积神经网络被应用到许多计算机视觉问题中。后来,条件生成对抗网络(CGAN)因其易于适应许多图像间问题而受到关注。CGANs已被广泛应用于各种任务,如背景掩蔽、分割、医学图像处理和超分辨率。在这项工作中,我们开发了一种基于GAN的方法来估计任何给定的单人脸图像的深度图。许多GANs的变体已经被测试用于的深度估计这项工作任务。我们的结论是,条件式瓦瑟斯坦GAN结构提供了最稳健的方法。我们还将该方法与其它两种基于深度学习和传统方法的方法进行了比较,实验结果表明,WGAN为从人脸图像中估计人脸深度图提供了很好的机会。

论文作者

Abdullah Taha Arslan,Erol Seke

关键字

三维人脸重建,生成对抗网络,深度学习

百度链接

链接: https://pan.baidu.com/s/13zk5uEeuGw7f5VyL9xAong 密码: 2bgb

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Generative Adversarial Networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they were trained to replicate. One recurrent theme in medical imaging is whether GANs can also be effective at generating workable medical data as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study to gauge the benefits of GANs in medical imaging. We tested various GAN architectures from basic DCGAN to more sophisticated style-based GANs on three medical imaging modalities and organs namely : cardiac cine-MRI, liver CT and RGB retina images. GANs were trained on well-known and widely utilized datasets from which their FID score were computed to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images. Results reveal that GANs are far from being equal as some are ill-suited for medical imaging applications while others are much better off. The top-performing GANs are capable of generating realistic-looking medical images by FID standards that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggests that no GAN is capable of reproducing the full richness of a medical datasets.

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最新论文

Generative Adversarial Networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they were trained to replicate. One recurrent theme in medical imaging is whether GANs can also be effective at generating workable medical data as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study to gauge the benefits of GANs in medical imaging. We tested various GAN architectures from basic DCGAN to more sophisticated style-based GANs on three medical imaging modalities and organs namely : cardiac cine-MRI, liver CT and RGB retina images. GANs were trained on well-known and widely utilized datasets from which their FID score were computed to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images. Results reveal that GANs are far from being equal as some are ill-suited for medical imaging applications while others are much better off. The top-performing GANs are capable of generating realistic-looking medical images by FID standards that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggests that no GAN is capable of reproducing the full richness of a medical datasets.

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