On the public citation graph
Denoising Autoencoder and U-Net-Based Anomaly Detection from Video Frames Using Vgg16 and Mobilenetv2 Classifier - a Deep Learning Hybrid Approach
0 signed · 21 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/int.22582openalex-extracted
Improved autoencoder for unsupervised anomaly detection
inferredcites →https://doi.org/10.1016/j.aei.2020.101105openalex-extracted
Anomaly detection of defects on concrete structures with the convolutional autoencoder
inferredcites →https://doi.org/10.1016/j.asoc.2019.106017openalex-extracted
APAD: Autoencoder-based Payload Anomaly Detection for industrial IoE
inferredcites →https://doi.org/10.1016/j.asoc.2023.110176openalex-extracted
A comprehensive survey on design and application of autoencoder in deep learning
inferredcites →https://doi.org/10.1016/j.neucom.2021.12.093openalex-extracted
GAN-based anomaly detection: A review
inferredcites →https://doi.org/10.1109/access.2021.3116612openalex-extracted
Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset
inferredcites →https://doi.org/10.1109/ccwc.2018.8301729openalex-extracted
Compressed residual-VGG16 CNN model for big data places image recognition
inferredcites →https://doi.org/10.1109/centcon52345.2021.9687944openalex-extracted
A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification
inferredcites →https://doi.org/10.1109/cscloud.2017.39openalex-extracted
Network Anomaly Detection with Stochastically Improved Autoencoder Based Models
inferredcites →https://doi.org/10.1109/icc.2018.8422402openalex-extracted
Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT
inferredcites →https://doi.org/10.1109/iccv.2019.00179openalex-extracted
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection
inferredcites →https://doi.org/10.1109/icfpt47387.2019.00072openalex-extracted
Real-Time Anomaly Detection for Flight Testing Using AutoEncoder and LSTM
inferredcites →https://doi.org/10.1109/icit.2019.8755116openalex-extracted
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder
inferredcites →https://doi.org/10.1109/tim.2022.3212547openalex-extracted
Unsupervised Machine Anomaly Detection Using Autoencoder and Temporal Convolutional Network
inferredcites →https://doi.org/10.11591/ijai.v10.i1.pp84-92openalex-extracted
Effect of data-augmentation on fine-tuned CNN model performance
inferredcites →https://doi.org/10.1166/jctn.2020.8648openalex-extracted
An Overview of Deep Learning Architecture of Deep Neural Networks and Autoencoders
inferredcites →https://doi.org/10.3390/app112311185openalex-extracted
An Improved VGG16 Model for Pneumonia Image Classification
inferredcites →https://doi.org/10.48550/arxiv.1901.03407openalex-extracted
Deep Learning for Anomaly Detection: A Survey
inferredcites →https://doi.org/10.48550/arxiv.2001.03674openalex-extracted
Semi-supervised Anomaly Detection using AutoEncoders
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.