On the public citation graph

A Semi-Supervised Multiscale Generalized-Vae Framework for One-Class Classification

https://doi.org/10.2139/ssrn.4862337
0 signed · 36 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.
Claim with ORCID
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-21074-8_31openalex-snapshot
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-45823-6_67openalex-snapshot
A Hybrid Autoencoder and Density Estimation Model for Anomaly Detection
inferredcites →https://doi.org/10.1007/978-3-319-59050-9_12openalex-snapshot
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
inferredcites →https://doi.org/10.1007/978-3-319-71249-9_3openalex-snapshot
Robust, Deep and Inductive Anomaly Detection
inferredcites →https://doi.org/10.1007/978-3-319-99492-5_8openalex-snapshot
One-Class Semi-supervised Learning
inferredcites →https://doi.org/10.1007/978-3-642-21735-7_7openalex-snapshot
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
inferredcites →https://doi.org/10.1016/j.dib.2019.104863openalex-snapshot
Dataset of breast ultrasound images
inferredcites →https://doi.org/10.1016/j.neucom.2023.126589openalex-snapshot
Ensemble anomaly score for video anomaly detection using denoise diffusion model and motion filters
inferredcites →https://doi.org/10.1016/j.neucom.2024.127444openalex-snapshot
DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection
inferredcites →https://doi.org/10.1016/j.patcog.2006.07.009openalex-snapshot
Kernel PCA for novelty detection
inferredcites →https://doi.org/10.1016/j.patcog.2016.03.028openalex-snapshot
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
inferredcites →https://doi.org/10.1023/b:mach.0000008084.60811.49openalex-snapshot
Support Vector Data Description
inferredcites →https://doi.org/10.1038/nmeth.2083openalex-snapshot
Annotated high-throughput microscopy image sets for validation
inferredcites →https://doi.org/10.1080/00949650802290912openalex-snapshot
Simulation techniques for generalized Gaussian densities
inferredcites →https://doi.org/10.1109/cvpr.2014.483openalex-snapshot
Unsupervised One-Class Learning for Automatic Outlier Removal
inferredcites →https://doi.org/10.1109/cvpr.2019.00982openalex-snapshot
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
inferredcites →https://doi.org/10.1109/dicta.2018.8615818openalex-snapshot
Whitening Pre-Filters with Circular Symmetry for Anomaly Detection in Hyperspectral Imagery
inferredcites →https://doi.org/10.1109/dicta60407.2023.00045openalex-snapshot
Deep Semi-supervised Anomaly Detection Using VQ-VAE
inferredcites →https://doi.org/10.1109/dsaa49011.2020.00096openalex-snapshot
Cluster Quality Analysis Using Silhouette Score
inferredcites →https://doi.org/10.1109/iccv.2015.177openalex-snapshot
Learning Discriminative Reconstructions for Unsupervised Outlier Removal
inferredcites →https://doi.org/10.1109/icme46284.2020.9102818openalex-snapshot
Glad: Global And Local Anomaly Detection
inferredcites →https://doi.org/10.1109/icme52920.2022.9859637openalex-snapshot
Domain-Generalized Textured Surface Anomaly Detection
inferredcites →https://doi.org/10.1109/isbi.2018.8363640openalex-snapshot
Abnormality detection using deep neural networks with robust quasi-norm autoencoding and semi-supervised learning
inferredcites →https://doi.org/10.1109/isbi48211.2021.9433778openalex-snapshot
Anomaly Detection Through Latent Space Restoration Using Vector Quantized Variational Autoencoders
inferredcites →https://doi.org/10.1109/isbi52829.2022.9761472openalex-snapshot
Robust and Uncertainty-Aware VAE (RU-VAE) for One-Class Classification
inferredcites →https://doi.org/10.1109/tpami.2021.3129349openalex-snapshot
Future Frame Prediction Network for Video Anomaly Detection
inferredcites →https://doi.org/10.1109/wacv51458.2022.00137openalex-snapshot
A Semi-supervised Generalized VAE Framework for Abnormality Detection using One-Class Classification
inferredcites →https://doi.org/10.1145/2733381openalex-snapshot
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
inferredcites →https://doi.org/10.1145/279943.279962openalex-snapshot
Combining labeled and unlabeled data with co-training
inferredcites →https://doi.org/10.1145/3097983.3098052openalex-snapshot
Anomaly Detection with Robust Deep Autoencoders
inferredcites →https://doi.org/10.1613/jair.3623openalex-snapshot
Toward Supervised Anomaly Detection
inferredcites →https://doi.org/10.48550/arxiv.0909.5422openalex-snapshot
Laplacian Support Vector Machines Trained in the Primal
inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot
Auto-Encoding Variational Bayes
inferredcites →https://doi.org/10.48550/arxiv.1805.08498openalex-snapshot
Implicit Reparameterization Gradients
inferredcites →https://doi.org/10.5555/1953048.2021038openalex-snapshot
Laplacian Support Vector Machines Trained in the Primal
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.