Results 41 to 50 of about 75,818 (178)

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, Sautière, Guillaume, Ryu, J. Jon, Cohen, Taco S   +3 more
openaire   +2 more sources

Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management

International Journal of Information Management, 2020
Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management.
H. D. Nguyen, K. Tran, S. Thomassey, Moez Hamad   +3 more
semanticscholar   +1 more source

Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset

IEEE Access, 2021
Network anomaly detection plays a crucial role as it provides an effective mechanism to block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there has been a number of Autoencoder (AE) based deep learning approaches ...
Wen Xu, Julian Jang-Jaccard, Amardeep Singh, Yuanyuan Wei, F. Sabrina   +4 more
semanticscholar   +1 more source

Anomaly sign detection by monitoring thousands of process values using a two-stage autoencoder

Mechanical Engineering Journal, 2021
In a large-scale plant such as a nuclear power plant, thousands of process values are measured for the purpose of monitoring the plant performance and the health of various systems.
Susumu NAITO, Yasunori TAGUCHI, Yuichi KATO, Kouta NAKATA, Ryota MIYAKE, Isaku NAGURA, Shinya TOMINAGA, Toshio AOKI   +7 more
doaj   +1 more source

LLNet: A deep autoencoder approach to natural low-light image enhancement [PDF]

Pattern Recognition, 2015
In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission.
Kin Gwn Lore, Adedotun Akintayo, S. Sarkar   +2 more
semanticscholar   +1 more source

MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction [PDF]

IEEE International Conference on Computer Vision, 2017
In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with
A. Tewari, M. Zollhöfer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, P. Pérez, C. Theobalt   +6 more
semanticscholar   +1 more source

Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization [PDF]

IEEE International Conference on Computer Vision, 2017
In this paper, we propose a new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments.
Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong (Tom) Cai, Heng Huang   +4 more
semanticscholar   +1 more source

Single-cell RNA-seq denoising using a deep count autoencoder

Nature Communications, 2018
Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-
Gökçen Eraslan, L. Simon, M. Mircea, N. Mueller, Fabian J Theis   +4 more
semanticscholar   +1 more source

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, Miao He, Jason Deutsch, David He   +3 more
doaj   +1 more source

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, QI Zisen, XU Hua, SHI Yunhao   +3 more
doaj   +1 more source