Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning

Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones. In practice, however, this assumption is unreliable in the unsupervised case, where the training data...

书目详细资料
Main Authors:	Nicholas Merrill, Azim Eskandarian
格式:	文件
语言:	English
出版:	IEEE 2020-01-01
丛编:	IEEE Access
主题:	Hyperspectral HSI deep learning anomaly detection unsupervised autoencoder
在线阅读:	https://ieeexplore.ieee.org/document/9099561/

相似书籍

Robust multi-stage progressive autoencoder for hyperspectral anomaly detection
由: Qing Guo, et al.
出版: (2024-11-01)

A cascaded autoencoder unmixing network for Hyperspectral anomaly detection
由: Kun Li, et al.
出版: (2025-02-01)

Pixel-associated autoencoder for hyperspectral anomaly detection
由: Pei Xiang, et al.
出版: (2024-05-01)

Hyperspectral Anomaly Detection Based on Spatial–Spectral Cross-Guided Mask Autoencoder
由: Qing Guo, et al.
出版: (2024-01-01)

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction
由: Yang Song, et al.
出版: (2024-10-01)

Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification
由: Qiaoqiao Sun, et al.
出版: (2021-04-01)

Hyperspectral anomaly detection via memory‐augmented autoencoders
由: Zhe Zhao, et al.
出版: (2023-12-01)

Unsupervised Transformer Boundary Autoencoder Network for Hyperspectral Image Change Detection
由: Song Liu, et al.
出版: (2023-03-01)

Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
由: İrem Üstek, et al.
出版: (2024-11-01)

Instructional Mask Autoencoder: A Scalable Learner for Hyperspectral Image Classification
由: Weili Kong, et al.
出版: (2024-01-01)

Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network
由: Jiahao Shan, et al.
出版: (2024-01-01)

Quantized non-volatile nanomagnetic domain wall synapse based autoencoder for efficient unsupervised network anomaly detection
由: Muhammad Sabbir Alam, et al.
出版: (2024-01-01)

Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images
由: Marius Giger, et al.
出版: (2024-02-01)

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network
由: Shanshan Jiang, et al.
出版: (2023-06-01)

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
由: Wenqiang Liu, et al.
出版: (2024-01-01)

Multiobjective Optimization-Based Hyperspectral Unsupervised Band Selection for Anomaly Detection
由: Shihui Liu, et al.
出版: (2025-01-01)

Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks
由: Atsuya Emoto, et al.
出版: (2025-01-01)

EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection
由: Yufei Liu, et al.
出版: (2021-09-01)

Anomaly-Guided Double Autoencoders for Hyperspectral Unmixing
由: Hongyi Liu, et al.
出版: (2025-02-01)

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection
由: Nan Wang, et al.
出版: (2023-09-01)

Unsupervised Anomaly Detection Applied to Φ-OTDR
由: Antonio Almudévar, et al.
出版: (2022-08-01)

Fractal Autoencoder-Based Unsupervised Hyperspectral Bands Selection for Remote Sensing Land-Cover Classification
由: Sara Benali, et al.
出版: (2023-11-01)

DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection
由: Xigang Zhao, et al.
出版: (2024-12-01)

FCAE-AD: Full Convolutional Autoencoder Based on Attention Gate for Hyperspectral Anomaly Detection
由: Xianghai Wang, et al.
出版: (2023-08-01)

JointNet: Multitask Learning Framework for Denoising and Detecting Anomalies in Hyperspectral Remote Sensing
由: Yingzhao Shao, et al.
出版: (2024-07-01)

An Unsupervised Momentum Contrastive Learning Based Transformer Network for Hyperspectral Target Detection
由: Yulei Wang, et al.
出版: (2024-01-01)

Anomaly Detection of Water Level Using Deep Autoencoder
由: Isack Thomas Nicholaus, et al.
出版: (2021-10-01)

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia
由: Serag Mohamed Akila, et al.
出版: (2023-06-01)

Soundscape Characterization Using Autoencoders and Unsupervised Learning
由: Daniel Alexis Nieto-Mora, et al.
出版: (2024-04-01)

Self-Supervised Autoencoders for Visual Anomaly Detection
由: Alexander Bauer, et al.
出版: (2024-12-01)

Anomaly Detection using LSTM AutoEncoder
由: Mahmoud Moallem, et al.
出版: (2019-04-01)

HSIMAE: A Unified Masked Autoencoder With Large-Scale Pretraining for Hyperspectral Image Classification
由: Yue Wang, et al.
出版: (2024-01-01)

Marble Surface Anomaly Detection Using Autoencoder Architecture
由: Yahya Abdullah, et al.
出版: (2024-05-01)

BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder
由: Dongqi Wang, et al.
出版: (2023-08-01)

Interpretability-Aware Industrial Anomaly Detection Using Autoencoders
由: Rui Jiang, et al.
出版: (2023-01-01)

Multi-scale frequency-guided two-stream network for hyperspectral anomaly detection
由: Zhe Zhao, et al.
出版: (2025-02-01)

CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection
由: Yuxiao Duan, et al.
出版: (2023-06-01)

Logarithmic Kernel Relaxed Collaborative Representation With Scaled MST Dictionary Construction for Hyperspectral Anomaly Detection
由: Yang Zhao, et al.
出版: (2024-01-01)

Anomaly Detection of Hyperspectral Images Based on Transformer With Spatial–Spectral Dual-Window Mask
由: Song Xiao, et al.
出版: (2023-01-01)

An End-to-End Underwater-Image-Enhancement Framework Based on Fractional Integral Retinex and Unsupervised Autoencoder
由: Yang Yu, et al.
出版: (2023-01-01)