Results 61 to 70 of about 119,888 (330)

Topological Autoencoders

CoRR, 2019
Proceedings of the 37th International Conference on Machine ...
Michael Moor, Max Horn, Bastian Rieck, Karsten M. Borgwardt   +3 more
openaire   +3 more sources

Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Applied Sciences, 2020
As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image ...
Young-Joo Han, Ha-Jin Yu
doaj   +1 more source

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [PDF]

Computer Vision and Pattern Recognition, 2020
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation problem,
Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, F. Saleh, Tong Zhang, Nick Barnes   +6 more
semanticscholar   +1 more source

An Introduction to Autoencoders

CoRR, 2022
In this article, we will look at autoencoders. This article covers the mathematics and the fundamental concepts of autoencoders. We will discuss what they are, what the limitations are, the typical use cases, and we will look at some examples. We will start with a general introduction to autoencoders, and we will discuss the role of the activation ...
openaire   +2 more sources

An Efficient Neural Architecture Search Algorithm for AutoEncoder Optimization - A Systematic Literature Review

UMYU Scientifica Journal
Autoencoders have developed into neural search networks in recent years, and the majority of machine learning (ML) methods rely on the input properties to produce high-quality models.
Samuel Michael Ogbe, Ahmed Aliyu Abubakar   +1 more
doaj   +1 more source

Comparison of methods for correcting outliers in ECG-based biometric identification

Metrology and Measurement Systems, 2020
The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG ...
Su Jun, Miroslaw Szmajda, Volodymyr Khoma, Yuriy Khoma, Dmytro Sabodashko, Orest Kochan, Jinfei Wang   +6 more
doaj   +1 more source

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection [PDF]

Network and Distributed System Security Symposium, 2018
Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network ...
Yisroel Mirsky, Tomer Doitshman, Y. Elovici, A. Shabtai   +3 more
semanticscholar   +1 more source

Sinkhorn AutoEncoders

CoRR, 2018
Accepted for oral presentation at ...
Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav 0001, Max Welling, Tim Genewein, Frank Nielsen   +7 more
openaire   +4 more sources

Symmetric Wasserstein Autoencoders

CoRR, 2021
37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, July 27-30, 2021, Virtual ...
Sun, Sun, Guo, Hongyu
openaire   +4 more sources

Robust reduced-order machine learning modeling of high-dimensional nonlinear processes using noisy data

Digital Chemical Engineering
Autoencoder-based reduced-order machine learning models have been developed for modeling and predictive control of nonlinear chemical processes with high dimensionality such as discretization of reaction–diffusion processes.
Wallace Gian Yion Tan, Ming Xiao, Zhe Wu
doaj   +1 more source