Cover Image

Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hojjati, Hadi, Ho, Thi Kieu Khanh, Armanfard, Narges
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph Anomaly Detection in Time Series: A Survey
by: Ho, Thi Kieu Khanh, et al.
Published: (2023)

Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
by: Ho, Thi Kieu Khanh, et al.
Published: (2023)

DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
by: Hojjati, Hadi, et al.
Published: (2021)

ARTA: Adversarial-Robust Multivariate Time--Series Anomaly Detection via Sparsity-Constrained Perturbations
by: Hojjati, Hadi, et al.
Published: (2026)

Open-Set Multivariate Time-Series Anomaly Detection
by: Lai, Thomas, et al.
Published: (2023)

Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection
by: Koran, Alex, et al.
Published: (2024)

EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset
by: Hojjati, Hadi, et al.
Published: (2026)

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
by: Karami, Ali, et al.
Published: (2024)

ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
by: Wei, Jack Yi, et al.
Published: (2026)

Deep Clustering with Self-Supervision using Pairwise Similarities
by: Sadeghi, Mohammadreza, et al.
Published: (2024)

Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets
by: Koran, Alex, et al.
Published: (2026)

TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
by: Cureton, Cormac, et al.
Published: (2026)

Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning
by: Nikpour, Bahareh, et al.
Published: (2023)

Cross-Task Affinity Learning for Multitask Dense Scene Predictions
by: Sinodinos, Dimitrios, et al.
Published: (2024)

MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
by: Sinodinos, Dimitrios, et al.
Published: (2025)

Forward-Backward Knowledge Distillation for Continual Clustering
by: Sadeghi, Mohammadreza, et al.
Published: (2024)

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
by: Liu, Yidan, et al.
Published: (2024)

End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection
by: Deforce, Boje, et al.
Published: (2024)

SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
by: Li, Elvin, et al.
Published: (2025)

Self-Tuning Self-Supervised Image Anomaly Detection
by: Yoo, Jaemin, et al.
Published: (2023)

Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
by: Wang, Zihan, et al.
Published: (2025)

Self-Supervised Training with Autoencoders for Visual Anomaly Detection
by: Bauer, Alexander, et al.
Published: (2022)

GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification
by: Buchta, Robin, et al.
Published: (2026)

A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges
by: Sánchez-Ferrera, Aitor, et al.
Published: (2025)

Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction
by: Sinodinos, Dimitrios, et al.
Published: (2026)

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection
by: Sánchez-Ferrera, Aitor, et al.
Published: (2025)

Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
by: Chen, Yutong, et al.
Published: (2024)

Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
by: Li, Zhong, et al.
Published: (2025)

A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
by: Khan, Asifullah, et al.
Published: (2024)

Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
by: Zhou, Yingjie, et al.
Published: (2026)

Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
by: Aqeel, Muhammad, et al.
Published: (2024)

Anomaly Detection Based on Isolation Mechanisms: A Survey
by: Cao, Yang, et al.
Published: (2024)

Statistical Inference for Clustering-based Anomaly Detection
by: Phu, Nguyen Thi Minh, et al.
Published: (2025)

Self-Supervised Learning by Curvature Alignment
by: Ghojogh, Benyamin, et al.
Published: (2025)

Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation
by: Choi, Kukjin, et al.
Published: (2024)

A Survey on Diffusion Models for Anomaly Detection
by: Liu, Jing, et al.
Published: (2025)

Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
by: Dong, Zhijin, et al.
Published: (2024)

Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
by: Zhao, Haihong, et al.
Published: (2024)

Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond
by: Mehta, Shanay, et al.
Published: (2024)

Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey
by: Huang, Haoqi, et al.
Published: (2025)