Results 31 to 40 of about 56,672 (314)
Comparison of Variation Autoencoder with Autoencoder Ala Principe [PDF]
, 2022 In the past few years Generative models have become an interesting topic in the field of Machine Learning (ML). Variational Autoencoder (VAE) is one of the popular frameworks of generative models based on the work of D.P Kingma and M.
Naredla, Santhosh
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Enhancing Radar Resolution and Target Detection Probability with a Denoising Autoencoder [PDF]
Journal of Electromagnetic Engineering and ScienceWe propose the use of a denoising autoencoder to improve radar resolution and target detection probability in noise-contaminated range-Doppler diagrams. Conventionally, target detection has been performed using constant false alarm rate (CFAR) algorithms,
Wonhyo Kim, Daegun Oh, Youngwook Kim
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Error correction algorithm of array time-varying amplitude and phase based on autoencoder
Xibei Gongye Daxue Xuebao, 2023 As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology.
ZHANG Zixuan +3 more
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, 2023 Variability of the shallow autoencoder on the Sim1 dataset (17 clusters) over 100 executions, where the white star represents the average score over 100. An important note is that DBS has lower scores for a higher performance and as DBS and CHS have only
Ana-Maria Ichim (14648702) +4 more
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Feedback Recurrent Autoencoder [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be ...
Yang Yang 0010 +3 more
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CoRR, 2019 Proceedings of the 37th International Conference on Machine ...
Michael Moor +3 more
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Structuring Autoencoders [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data which are additionally enriched with a desired structure in this low dimensional space. While traditional Autoencoders have proven to structure data naturally they fail to discover semantic structure that is hard to ...
Marco Rudolph +2 more
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CoRR, 2023 The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF ...
Faris Janjos +3 more
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Detection of Pitting in Gears Using a Deep Sparse Autoencoder
Applied Sciences, 2017 In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network.
Yongzhi Qu +3 more
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