Semi-supervised Anomaly Detection

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

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 37 papers with code (76 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.

• EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
  25 Mar 2023 33 repositories listed Syntology ran 1 of 35 samples · 34 unverified
  We train a student network to predict the extracted features of normal, i.
• Real-world Anomaly Detection in Surveillance Videos
  12 Jan 2018 9 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 4 pointer-only (licence)
  To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled…
• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
  17 May 2018 8 repositories listed Syntology ran 0 of 23 samples · 23 unverified
  Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the…
• Deep Semi-Supervised Anomaly Detection
  6 Jun 2019 7 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
• Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
  6 Jan 2017 5 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  We present an efficient method for detecting anomalies in videos.
• Deep Weakly-supervised Anomaly Detection
  30 Oct 2019 4 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by…
• Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
  21 Aug 2023 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and…
• ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
  15 Aug 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  To overcome this bottleneck, we leverage class priors to restrict the generalization scope of the class-agnostic SAM and propose a class-aware smoothness optimization algorithm named Imbalanced-SAM (ImbSAM).
• Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
  3 Feb 2023 2 repositories listed
  When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution).
• Learning Temporal Regularity in Video Sequences
  15 Apr 2016 2 repositories listed
  Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
• PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series
  21 Nov 2024 1 repository listed
  To cater for both unsupervised and semi-supervised anomaly detection settings, as well as time series generation and forecasting, we make different versions of the dataset available, where training and test subsets are…
• SADDE: Semi-supervised Anomaly Detection with Dependable Explanations
  18 Nov 2024 1 repository listed
  In this research paper, we introduce SADDE, a general framework designed to accomplish two primary objectives: (1) to render the anomaly detection process interpretable and enhance the credibility of interpretation…
• Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
  16 Jul 2024 1 repository listed
  Using the variance norm, we introduce the notion of a kernelized nearest-neighbour Mahalanobis distance.
• Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
  29 May 2024 1 repository listed
  With our approach, we can approximate the anomaly scores for normal data using the unlabeled and anomaly data.
• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image
  21 May 2024 1 repository listed
  Medical anomaly detection is a critical research area aimed at recognizing abnormal images to aid in diagnosis.
• Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
  11 May 2024 1 repository listed
  The model leverages causal inference to extract the intrinsic causal feature in data, enhancing the agent's utilization of prior knowledge and improving its generalization capability.
• MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection
  10 May 2024 1 repository listed
  Meanwhile, a memory-enhanced learning mechanism is introduced to effectively predict abnormal regions by analyzing the difference be-tween the input samples and the normal samples in the memory pool.
• Machine learning-based identification of Gaia astrometric exoplanet orbits
  14 Apr 2024 1 repository listed
  We developed an alternative machine learning approach that uses only the Gaia DR3 orbital solutions with the aim of identifying the best candidates for exoplanets and brown-dwarf companions.
• NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
  20 Nov 2023 1 repository listed
  While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the…
• RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
  25 Jul 2023 1 repository listed
  anomaly contamination.
• AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
  30 May 2023 1 repository listed
  Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data.
• On Diffusion Modeling for Anomaly Detection
  29 May 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE).
• SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
  23 May 2023 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
• R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
  21 Jun 2022 1 repository listed
  Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss.
• Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning
  30 Apr 2022 1 repository listed
  Network anomaly detection is a crucial task since a few anomalies can cause huge losses.
• 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.
• Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees
  1 Jan 2021 1 repository listed
  Given two different anomaly score functions, we formally define their difference in performance as the relative scoring bias of the anomaly detectors.
• Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
  27 Oct 2020 1 repository listed
  For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0.
• Graph Fairing Convolutional Networks for Anomaly Detection
  20 Oct 2020 1 repository listed
  The proposed layerwise propagation rule of our model is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from…

Syntology lines on 10 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.