Dynamic Network Embedding by Using Sparse Deep Autoencoder

Tang, Huimei; Shao, Zengyang; Zhang, Yutao; Ma, Lijia; Lin, Qiuzhen

doi:10.1007/978-3-031-20503-3_11

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…But it ensures that a small number of hidden layer units are active for input data. In this way, it enables a model to concentrate on some input features, which makes it convenient to obtain the unique features of the input data (Goodfellow et al, 2019, Makhzani & Frey, 2013, Ng, 2011). DAE is an autoencoder that is trained to find the input data as original data (Jiang et al, 2017; Meng et al, 2018; Vincent et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders

Çelik

Karabatak

2021

Expert Systems

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Background and objective Artificial intelligence‐based analysis of medical images has recently become a trendy field of study. The most significant effect on these systems to produce reliable and high‐performance results is the amount of accessed data. Although whole‐slide images (WSI), one of the current imaging techniques used in histopathology, storage costs are very expensive. Therefore, low‐dimensional visuals representing of WSIs is an essential field of study. Methods In this study, deep auto‐encoder‐based models are designed to create low dimensional representations for WSI. The size reduction was performed for the input images in different sizes, at the ratios of 1: 3, 1: 6, and 1: 12, respectively. Results Similarity index measure values were obtained as high as 0.957 and 0.938, respectively, using 128 × 128 × 3 dimensional patches in the size reduction process of 1: 3 and 1:12 on test WSI. Conclusion The proposed model has a structure that can be applied in compression of WSIs and secure data transfer. By using these representations, both storage costs and the high‐level hardware costs required by deep learning algorithms can be significantly reduced.

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Section: Methodsmentioning

confidence: 99%

Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders

Çelik

Karabatak

2021

Expert Systems

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“…In the past few years, the deep learning technology has been widely used in radar emitter identification field [31–35], reflecting the integration of the artificial intelligence technology and radar identification. Auto encoder (AE) [36, 37], as an unsupervised network, has achieved breakthroughs in image recognition [2] and speech recognition [38, 39]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Radar emitter structure identification based on stacked frequency sparse auto‐encoder network

Liu,

Zhang,

Song

et al. 2023

IET Radar Sonar & Navi

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In the current complex situations of electronic intelligence (ELINT), the authors present a radar emitter structure (RES) identification method based on deep learning at a new level to address the issue of incomplete cognitive information. Firstly, due to the fact that existing simulation data cannot fully reflect the structure features of the entire radar emitter, the structure feature‐level RES model is built using direct digital synthesiser (DDS) technology and radio frequency (RF) simulation platform. Afterwards, considering that the structure features are reflected in the frequency domain, a stacked frequency sparse auto encoder (sFSAE) network is constructed by adding a constraint term in frequency domain to the loss function of sparse auto encoder (SAE). Using deep learning to extract structure features with constraints in different domains is instructive for feature extraction techniques under variable operating parameters. Finally, the extracted structure features are input into the Softmax classifier to perform the identification from the radar signal to the RES. The experimental results show that the proposed method has high generalisation ability and robustness under different modulation types, different operating parameters and different signal to noise ratio (SNR). It also has a high identification rate even for untrained modulated signals.

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Dynamic Network Embedding by Using Sparse Deep Autoencoder

Cited by 2 publications

References 26 publications

Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders

Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders

Radar emitter structure identification based on stacked frequency sparse auto‐encoder network

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