今天给大家介绍帝国理工学院的Antonia Creswell等人在IEEE Transactions on Neural Networks and Learning Systems上发表的文章” Inverting the Generator of a Generative Adversarial Network”。生成性抗网络(Generative Adversarial Network，GAN)能够生成新的数据样本。生成模型可以从选定的先验分布中提取的潜在样本来合成新...
In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the discriminator. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). ...
结合其他生成模型思想及其他领域思想对GAN 模型进行改进,同样可以起到优化模型表现或拓展模型应用场景的效果。 DCGAN 使用去除池化层的 CNN(convolutional neural network)替代基本 GAN 模型中的多层感知机(如图 30所示),并使用全局池化层替代全连接层以减少计算量,以提高生成样本的质量,优化训练不稳定的问题。
具体代码如下。 self.G = Sequential() dropout = 0.4 depth = 64+64+64+64 dim = 7 # In: 100 # Out: dim x dim x depth self.G.add(Dense(dim*dim*depth, input_dim=100)) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation('relu')) self.G.add(Reshape((dim, dim, ...
Steganographicgenerative adversarial networks. arXiv preprint arXiv:1703.05502, 2017. [18] Shin, Hanul, et al. "Continual learning with deep generative replay." Advances in Neural Information Processing Systems. 2017. 本文为机器之心转载,转载请联系原公众号获得授权。
^《Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information ...
[12] Salimans, Tim, et al. "Improved techniques for training gans." Advances in neural information processing systems. 2016. [13] https://blog.csdn.net/qq_25737169/article/details/78532719 [14] https://medium.com/@hitoshinakanishi/reading-note-triple-generative-adversarial-nets-fc3775e52b1e1...
卷积神经网络(Convolutional Neural Network, CNN)通过平移、缩放和旋转不变性在图像处理领域中被广泛应用,通过使用CNN可以生成质量更好的样本图像并可以解决标准GAN模型中训练过程不稳定等问题[13]。自编码神经网络具有较强的特征学习能力,通...
[7] Liu M, Tuzel O. Coupled Generative Adversarial Networks[J]. neural information processing systems, 2016: 469-477. 【模型解读】从2D卷积到3D卷积,都有什么不一样 【模型解读】浅析RNN到LSTM 【模型解读】历数GAN的5大基本结构 阿里天池模型结构设计与优化直播: ...
10. Generalization and Equilibrium in Generative Adversarial Nets (GANs)泛化能力是衡量生成模型好坏的一个重要标准，本文指出 JS 散度、Wasserstein 距离都不能很好地衡量模型的泛化能力，为此，文中提出了一种基于 discriminator 的 neural network divergence/distance，用于衡量模型的泛化能力。此外作者还证明了 WGAN ...