Self-Supervised Anomaly Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (29 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
  30 Sep 2021 3 repositories listed Syntology ran 2 of 8 samples · 6 unverified
  We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data.
• Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis
  30 Aug 2024 1 repository listed
  Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets.
• An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
  16 Jul 2024 1 repository listed
  This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading…
• Confidence-Aware and Self-Supervised Image Anomaly Localisation
  23 Mar 2023 1 repository listed
  Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis.
• Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
  16 Aug 2022 1 repository listed
  Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling.
• Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
  17 Jan 2022 1 repository listed
  Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution.
• SLA²P: Self-supervised Anomaly Detection with Adversarial Perturbation
  25 Nov 2021 1 repository listed
  Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these…
• Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
  16 Apr 2021 1 repository listed
  Although various approaches have been proposed to solve this problem, two major limitations exist: (1) unsupervised approaches usually work much less efficiently due to the lack of supervisory signal, and (2) existing…
• Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
  19 Feb 2021 1 repository listed
  Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.
• Iterative weak/self-supervised classification framework for abnormal events detection
  3 Jan 2021 1 repository listed
  The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.