Aiming at this problem, this paper proposed an unsupervised learning algorithm named Memory-augmented skip-connected deep autoencoder (Mem-SkipAE) for anomaly detection of rocket engines with multi-source data fusion. Unlike traditional autoencoders, the input embedding for the decoder is not ...
The first and third Conv2D blocks are connected via a skip link. The skip connection keeps early layer features, improving the model’s learning capabilities, by adding the output of the first and third convolutional blocks. This operation is performed in the adding layer, then the output of ...
We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recen... Y Jin,S Kuwashima,T Kurita 被引量: 14发表: 2017年 Dynamic Frame skip Deep Q Network Deep Reinforcement Learning methods have achieved...
Generally, the architecture of a convolutional encoder is regarded as an integration of a feature learning layer, a nonlinear transform layer, a normalization layer, and a feature pooling layer. At the feature learning layer, the input of each unit is connected to the output of the previous ...
Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708. [Google Scholar] Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K. Aggregated residual transformations for ...
SCAE-MT [51] designs a stacked convolutional self-coding network model to extract deep features of hyperspectral remote sensing images. Some studies, such as SAE [52], DNR [53], and SDNE [54], use autoencoder to embed the network representation. However, these works do not resolve our ...
Notably, Atteia proposed a hybrid DL system combining autoencoder networks for feature representation learning in the latent space with the feature extraction abilities of standard pre-trained convolutional neural networks (CNNs) [1]. Chand and Vishwakarma proposed a novel DL framework (DLF) based ...
SCAE-MT [51] designs a stacked convolutional self-coding network model to extract deep features of hyperspectral remote sensing images. Some studies, such as SAE [52], DNR [53], and SDNE [54], use autoencoder to embed the network representation. However, these works do not resolve our ...
SCAE-MT [51] designs a stacked convolutional self-coding network model to extract deep features of hyperspectral remote sensing images. Some studies, such as SAE [52], DNR [53], and SDNE [54], use autoencoder to embed the network representation. However, these works do not resolve our ...