Convolutional autoencoder networkImage completionSkip connectionsThis paper proposes an effective image inpainting method using an improved deep convolutional auto-encoder network. By analogy with exiting methods of image inpainting based on auto-decoders, inpainting methods using the deep convolutional auto-...
In Ref. [53], state variables are compressed using an autoencoder and the resulting dynamics learned and predicted using a RNN. A similar approach is taken in Ref. [49], with a LSTM network replacing the RNN. Besides direct prediction of state variables, such techniques have also been ...
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network
Graph Convolutional Network可以从两个角度来理解。 第一个角度,也称空域GCN,是Graph + Convolutional Network,在graph上运用CNN的思想。 传统的全连接网络参数很多,但CNN利用图像的局部平移不变性,可以通过focus局部特征来减少参数量。放到图上也是这个道理。一个节点的信息跟其他节点都有关系,如果把每个节点都用所有多...
Alex Bronstein, Michael Bronstein, Deformable Shape Completion with Graph Convolutional Autoencoders,...
Recently, various deep learning approaches have been applied to the network intrusion detection area, such as restricted Boltzmann machines (RBMs), deep belief networks (DBNs), stacked autoencoders (SAEs), and supervised learning with convolutional neural networks (CNNs). The existing work about the...
卷积神经网络(CNN)是多层前向网络(MLPs)的一种变体,而MLP是受生物学的启发发展而来的。从Hubel和Wiesel对猫的视觉皮层(visual contex)所做的早期工作,我们可以看出视觉皮层中的神经元(cells)分布十分复杂。每个神经元仅对视觉域(visual field)的某个局部区域较为敏感,这个区域叫做感受野(receptive field)。这些局部区...
The loss error between the original input of the encoder and output of the decoder is used as the loss error to train the resulting model. Figure 1 shows a pictorial representation of the autoencoder network model. Figure 1 The pictorial representation of an autoencoder network model. Full ...
Pooling can complicate some kinds of neural network architectures that use top-down information, such as Boltzmann machines and autoencoders. These issues will be discussed further when we present these types of networks in part III . Pooling in convolutional Boltzmann machines is presented in sectio...