Search Results - sparse conventional autoencoder ((same OR sage))*

Masked autoencoder for highly compressed single-pixel imaging by Liu, Haiyan, Chang, Xuyang, Yan, Jun, Guo, Pengyu, Xu, Dong, Bian, Liheng

“… In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules…”
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Deep learning for pixel-level image fusion: Recent advances and future prospects by Liu, Yu, Chen, Xun, Wang, Zengfu, Wang, Z. Jane, Ward, Rabab K., Wang, Xuesong

“…•The difficulties that exist in conventional image fusion research are analyzed.•The advantages of deep learning (DL…”
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Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder by Wang, Peng-Fei, Luo, Xiao-Qing, Li, Xin-Yi, Zhang, Zhan-Cheng

“…Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data…”
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Application of the robust autoencoder to reduce reverberation and facilitate underwater target tracking by Xiang, Wenjie, Song, Zhongchang, Gao, Zhanyuan, Yang, Wuyi, Zhang, Boyu, Yang, Hongjun, Tu, Jianqiu, Li, Baoyu, Zhang, Hairui, Zhang, Yu

“… To improve the accuracy of target detection under reverberation conditions, a novel sparse track-before-detect algorithm integrating a robust autoencoder and a particle filter (PF-RAE-TBD…”
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Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach by Gao, Yang, Zhao, Pingqi, Li, Guofa, Li, Hao

“…ABSTRACT Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an…”
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ProteinVAE: Variational AutoEncoder for Translational Protein Design by Lyu, Suyue, Shahin Sowlati-Hashjin, Garton, Michael

“… This means that they are often not suitable for generating proteins with the most potential for high clinical impact, due to the additional challenges of sparse data and large size many…”
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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by Nalinipriya, G., Rama Sree, S., Radhika, K., Laxmi Lydia, E., Karim, Faten Khalid, Ishak, Mohamad Khairi, Mostafa, Samih M.

“… Recently, cybercriminals have become more complex with their approaches, though the underlying motives for conducting cyber threats remain largely the same…”
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A new Sparse Auto-encoder based Framework using Grey Wolf Optimizer for Data Classification Problem by Karim, Ahmad Mozaffer

“… Different training approaches are applied to train sparse autoencoders. Previous studies and preliminary experiments reveal that those approaches may present remarkable…”
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On the Performance of Deep Learning-based Data-aided Active User Detection for GF-SCMA System by Han, Minsig, Abebet, Ameha T., Kang, Chung G.

“…) in the receiver has demonstrated a significant performance improvement for grant-free sparse code multiple access (GF-SCMA) system…”
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On the Performance of Deep Learning-based Data-aided Active User Detection for GF-SCMA System by Han, Minsig, Ameha Tsegaye Abebe, Kang, Chung G

“…) in the receiver has demonstrated a significant performance improvement for grant-free sparse code multiple access (GF-SCMA) system…”
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A dense multi-path decoder for tissue segmentation in histopathology images by Vu, Quoc Dang, Kwak, Jin Tae

“…•We propose a dense multi-path decoder for tissue segmentation in histopathology images.•Convolutional neural networks are built upon the up-to-date encoders…”
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