95 Yang C F, Wu Q, Li H, Chen Y R. Generative poisoning attack method against neural networks. arXiv preprint arXiv: 1703.01340, 2017 96 Shen S, Jin G, Gao K, Zhang Y. APE-GAN: Adversarial Perturbation Elimination with GAN. arXiv preprint arXiv: 1707.05474, 2017. 97 Lee H, Han ...
二、Generative Adversarial Networks三、Speech Enhancement GAN四、实验步骤 4.1 数据集 4.2 SEGAN步骤五、结果 4.1 客观评价 4.2 主观评价六、总结七、致谢八、参考文献 论文地址:基于生成对抗网络的语音增强 博客地址(转载请指明出处):https://www.cnblogs.com/LXP-Never/p/9986744.html ...
隐式地用模型对数据的采样过程建模, 最出名的工作为使用了"博弈"思想训练的Generative Adversarial Network(GAN). 然而这两种模型在训练上都各有缺点: 第一种流派需要特殊设计模型架构使其能够对概率密度进行建模(p_{\theta}(\mathbf{x})=\frac{e^{-f_{\theta}(\mathbf{x})}}{Z_{\theta}}, 其中Z_{\...
Generative Adversarial NetworksDeep learningCloudServerlessSoftware servicesMicroservicesEnergy attacksService degradationThe role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide ...
Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network 摘要 尽管使用更快更深的卷积神经网络在单图像超分辨率的准确性和速度方面取得了突破,但仍有一个主要问题尚未解决:当使用大的上采样系数进行超分辨率时,我们怎样来恢复更精细的纹理细节。基于优化的超分辨率方法的行为主要由目标函数...
[10] S. Pascual, A. Bonafonte, and J. Serra, “SEGAN: speech enhancement generative adversarial network,” inINTERSPEECH. ISCA, Aug 2017, pp. 3642–3646. [11] D. Michelsanti and Z. H. Tan, “Conditional generative adversarial networks for speech enhancement and noiserobust speaker verifific...
[21] S. Pascual, A. Bonafonte, and J. Serra, “Segan: Speech en-hancement generative adversarial network,” in Proc. Interspeech, 2017. [22] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-toimage translation with conditional adversarial networks,” in arXiv:1611.07004, ...
本文中提出了用于跨模态MR图像合成的边缘感知生成对抗网络edge-aware generative adversarial networks(Ea-GANs)。具体地说,本文集成了边缘信息,它反映了图像内容的纹理结构,并描述了图像中不同对象的边界。针对不同的学习策略,本文提出了两个框架,generator-induced Ea-GAN(gEa-GAN)和discriminator-induced Ea-GAN (...
引用:Introductory guide to Generative Adversarial Networks (GANs) and their promise! 回到顶部 What is a GAN? Let us take an analogy to explain the concept: 如果你想在某件事上做到更好,例如下棋,你会怎么做? 你或许会找一个比自己厉害的对手. 然后你会在你们对决中分析你错的地方和他对的地方, 并...