On the public citation graph

RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

https://doi.org/10.24963/ijcai.2021/208

0 signed · 60 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-3-319-54765-7openalex-snapshot

Outlier Ensembles: An Introduction

inferredcites →https://doi.org/10.1007/978-981-16-2102-4_36openalex-snapshot

Anomaly Detection: A Survey

inferredcites →https://doi.org/10.1109/icip.2001.958946openalex-snapshot

One-class SVM for learning in image retrieval

inferredcites →https://doi.org/10.1109/tkde.2019.2905606openalex-snapshot

Generative Adversarial Active Learning for Unsupervised Outlier Detection

inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot

Anomaly detection

inferredcites →https://doi.org/10.1145/2689746.2689747openalex-snapshot

Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

inferredcites →https://doi.org/10.1145/3097983.3098052openalex-snapshot

Anomaly Detection with Robust Deep Autoencoders

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot

Auto-Encoding Variational Bayes

inferredcites →https://doi.org/10.48550/arxiv.1712.05055openalex-snapshot

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

inferredcites →https://doi.org/10.48550/arxiv.1804.06872openalex-snapshot

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

inferredcites →https://doi.org/10.48550/arxiv.1810.11874openalex-snapshot

Learning with Bad Training Data via Iterative Trimmed Loss Minimization

inferredcites →https://doi.org/10.48550/arxiv.2001.03316openalex-snapshot

Choosing the Sample with Lowest Loss makes SGD Robust

inferredcites →https://doi.org/10.5555/1756006.1953039openalex-snapshot

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4827142openalex-extracted

An Integrated Approach for Fault Detection and Diagnosis Based on Product Operating Data and Online Reviews

inferred← citeshttps://doi.org/10.1007/s10618-025-01102-wopenalex-snapshot

Deep anomaly detection with partition contrastive learning for tabular data

inferred← citeshttps://doi.org/10.1007/s13042-025-02700-2openalex-snapshot

Data-augmented robust multivariate anomaly detection for industrial cyber-physical systems with incomplete features

inferred← citeshttps://doi.org/10.1016/j.cose.2022.102658openalex-snapshot

MPSAutodetect: A Malicious Powershell Script Detection Model Based on Stacked Denoising Auto-Encoder

inferred← citeshttps://doi.org/10.1016/j.est.2025.118076openalex-snapshot

SL-DSVDD: Scale learning based deep support vector data description network for EV battery anomaly detection

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126922openalex-snapshot

Enhancing explainability in real-world scenarios: Towards a robust stability measure for local interpretability

inferred← citeshttps://doi.org/10.1016/j.iot.2025.101764openalex-snapshot

Enhancing big data analysis in IoT applications and optimizing the performance of machine learning models using hybrid dimensionality optimization approach

inferred← citeshttps://doi.org/10.1016/j.measurement.2024.115865openalex-snapshot

Towards training noise-robust anomaly detection via collaborative adversarial flows

inferred← citeshttps://doi.org/10.1016/j.patcog.2024.110395openalex-snapshot

Deterministic attribute selection for isolation forest

inferred← citeshttps://doi.org/10.1016/j.patcog.2024.110846openalex-snapshot

Unsupervised outlier detection using random subspace and subsampling ensembles of Dirichlet process mixtures

inferred← citeshttps://doi.org/10.1016/j.sysarc.2023.102861openalex-snapshot

Anomaly detection based on multi-teacher knowledge distillation

inferred← citeshttps://doi.org/10.1038/s41598-025-28976-6openalex-snapshot

A new hybrid model for improving outlier detection using combined autoencoder and variational autoencoder

inferred← citeshttps://doi.org/10.1080/01431161.2022.2112110openalex-snapshot

Hyperspectral anomaly detection based on score-guided cooperative autoencoders

inferred← citeshttps://doi.org/10.1093/bioadv/vbaf116openalex-snapshot

GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data

inferred← citeshttps://doi.org/10.1109/aann66429.2025.11257673openalex-snapshot

Stacked Autoencoder with Machine Learning for Anomaly Detection in Industrial Internet of Things

inferred← citeshttps://doi.org/10.1109/access.2024.3360691openalex-snapshot

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

inferred← citeshttps://doi.org/10.1109/cscwd68734.2026.11581631openalex-snapshot

Robust Anomaly Detection and Explainable Restoration in Satellite Positioning via Dual-Model Collaborative Consensus

inferred← citeshttps://doi.org/10.1109/dcoss-iot58021.2023.00022openalex-snapshot

(POSTER) Unsupervised and Condition Invariant Anomaly Detection for The Constrained Edge

inferred← citeshttps://doi.org/10.1109/dsit67006.2025.11390023openalex-snapshot

Anomaly Detection of Wellbore Data Based on Deep Hyperplane Segmentation of Isolated Forests

inferred← citeshttps://doi.org/10.1109/i2mtc66907.2026.11694961openalex-snapshot

Anomaly Detection Approach for LOS/NLOS Identification in 5G Systems

inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10888237openalex-snapshot

Diffusion Counterfactual-Based Anomaly Detection in Class-Imbalanced Data

inferred← citeshttps://doi.org/10.1109/iccit62134.2024.10701131openalex-snapshot

Digit Forest for Outlier Detection

inferred← citeshttps://doi.org/10.1109/icde65706.2026.00165openalex-snapshot

RFOD: Random Forest-Based Outlier Detection for Mixed-Type Tabular Data

inferred← citeshttps://doi.org/10.1109/icicml60161.2023.10424872openalex-snapshot

Credit Card Fraud Detection Using Adversarial Learning

inferred← citeshttps://doi.org/10.1109/ijcnn55064.2022.9892076openalex-snapshot

Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection

inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11228527openalex-snapshot

CCS-GAD: A Collaborative Contrastive Self-Supervised Learning Model for Graph Anomaly Detection

inferred← citeshttps://doi.org/10.1109/jiot.2024.3403711openalex-snapshot

Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework

inferred← citeshttps://doi.org/10.1109/jiot.2025.3616339openalex-snapshot

MRMamba: Multi-Resolution State-Space Modeling for Robust Multisensor Traffic Attack Detection in Wireless Sensor Networks

inferred← citeshttps://doi.org/10.1109/jsen.2024.3384275openalex-snapshot

Anomaly Detection Based on Data Super-Resolution in Industrial Cyber–Physical Systems With Multirate Sampling

inferred← citeshttps://doi.org/10.1109/phm-beijing63284.2024.10874552openalex-snapshot

Wafer map Anomaly Detection Driven by Contrast Learning and Vision Transformer Model

inferred← citeshttps://doi.org/10.1109/tai.2024.3394795openalex-snapshot

Quadratic Neuron-Empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection

inferred← citeshttps://doi.org/10.1109/tce.2024.3445629openalex-snapshot

A Partially Labeled Anomaly Data Detection Approach Based on Prioritized Deep Reinforcement Learning for Consumer Electronics Security

inferred← citeshttps://doi.org/10.1109/tevc.2024.3365101openalex-snapshot

A Bidirectional Differential Evolution-Based Unknown Cyberattack Detection System

inferred← citeshttps://doi.org/10.1109/tii.2023.3345461openalex-snapshot

Can Untrained Neural Networks Detect Anomalies?

inferred← citeshttps://doi.org/10.1109/tii.2024.3393491openalex-snapshot

AUTH: An Adversarial Autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs

inferred← citeshttps://doi.org/10.1109/tim.2025.3568969openalex-snapshot

Unsupervised Anomaly Detection for Aircraft PRSOV With Random Projection-Based Inner Product Prediction

inferred← citeshttps://doi.org/10.1109/tim.2025.3571122openalex-snapshot

Health-Aware Joint Learning of Scale Distribution and Compact Representation for Unsupervised Anomaly Detection in Photovoltaic Systems

inferred← citeshttps://doi.org/10.1109/tkde.2023.3270293openalex-snapshot

Deep Isolation Forest for Anomaly Detection

inferred← citeshttps://doi.org/10.1109/tkde.2024.3419891openalex-snapshot

Adversarial Graph Neural Network for Multivariate Time Series Anomaly Detection

inferred← citeshttps://doi.org/10.1109/tnnls.2025.3540602openalex-snapshot

Learning Decision Boundaries for Multidimensional Anomaly Detection

inferred← citeshttps://doi.org/10.1109/tnse.2025.3579809openalex-snapshot

System Log Anomaly Detection With Noise-Contrastive Learning and Pattern Feature

inferred← citeshttps://doi.org/10.1109/tpami.2025.3592089openalex-snapshot

M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising

inferred← citeshttps://doi.org/10.1145/3706117openalex-snapshot

Unsupervised Outlier Detection with Reinforced Noise Discriminator

inferred← citeshttps://doi.org/10.1145/3749177openalex-snapshot

Outliers: The Good, the Bad and the Ugly

inferred← citeshttps://doi.org/10.1186/s12859-026-06607-9openalex-snapshot

HCAM-BRCA: an interpretable multi-omics framework for breast cancer subtype classification and biomarker discovery

inferred← citeshttps://doi.org/10.2139/ssrn.5369248openalex-snapshot

Unsupervised Anomaly Detection in Ev Batteries Using Real-World Charging Data: A Multi-Scale Random Subspace Mapping Framework

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.