WGAN主要从损失函数的角度对GAN做了改进,损失函数改进之后的WGAN即使在全链接层上也能得到很好的表现结果。

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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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Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.

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最新论文

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.

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