Results 31 to 40 of about 34,284 (266)
Shock and Vibration, 2020 To enhance the performance of deep auto-encoder (AE) under complex working conditions, a novel deep auto-encoder network method for rolling bearing fault diagnosis is proposed in this paper. First, multiscale analysis is adopted to extract the multiscale
Jinyu Tong +4 more
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Object Recognition Algorithm Based on RGB Feature and Depth Feature Fusing [PDF]
Jisuanji gongcheng, 2016 Combining RGB image and depth image can effectively improve the RGB-D image recognition accuracy.However,prior researchers only do simple linear connect with the RGB image and depth features and do not extract and fuse the RGB and depth features ...
LU Liangfeng,XIE Zhijun,YE Hongwu
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BMC Medical Informatics and Decision Making, 2023 Background Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery.
Soheila Saeedi +3 more
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Conservativeness of Untied Auto-Encoders
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with anenergy function akin to the unnormalized log-probability of the data.We show that the conditions for conservativeness are more general than for encoder and decoder weights to be the same ("tied ...
Im, Daniel Jiwoong +2 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2022 We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses parameterized graph convolutional network (GCN) layers for its encoder and an ...
Kollias, Georgios +4 more
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Graph Attention Auto-Encoders [PDF]
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), 2020 Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage of relations in graph-structured data, several graph auto-encoders have recently been proposed, but they neglect to reconstruct either the graph structure
Salehi, Amin, Davulcu, Hasan
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, 2013 We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE).
Goroshin, Rostislav, LeCun, Yann
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, 2020 In this paper, a data-driven approach is proposed to jointly design the common sensing (measurement) matrix and jointly support recovery method for complex signals, using a standard deep auto-encoder for real numbers.
Cui, Ying, Li, Shuaichao, Zhang, Wanqing
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Backdoor Attack Method in Autoencoder End-to-End Communication System [PDF]
Jisuanji kexueEnd-to-end communication systems based on auto-encoders do not require an explicit design of communication protocols,resulting in lower complexity compared to traditional modular communication systems,as well as higher flexibility and robustness.However ...
GAN Run, WEI Xianglin, WANG Chao, WANG Bin, WANG Min, FAN Jianhua
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Multiple Description Coding Based on Convolutional Auto-Encoder
IEEE Access, 2019 Deep learning, such as convolutional neural networks, has been achieved great success in image processing, computer vision task, and image compression, and has achieved better performance. This paper designs a multiple description coding frameworks based
Hongfei Li +5 more
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