Outlier Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

11 leaderboard tables shown for this task, 11 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. 10 shown of 11 until expanded.

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

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

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 234 papers with code (703 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.

• Towards Total Recall in Industrial Anomaly Detection
  15 Jun 2021 18 repositories listed Syntology ran 5 of 36 samples · 31 unverified
  Being able to spot defective parts is a critical component in large-scale industrial manufacturing.
• LSTM Fully Convolutional Networks for Time Series Classification
  8 Sep 2017 9 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.
• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
  1 Jul 2016 8 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
• 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.
• Deep Sets
  10 Mar 2017 7 repositories listed Syntology ran 9 of 12 samples · 3 unverified · 9 pointer-only (licence)
  Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong.
• ADBench: Anomaly Detection Benchmark
  19 Jun 2022 5 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted…
• Adversarially Learned One-Class Classifier for Novelty Detection
  25 Feb 2018 5 repositories listed
  Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
• Generalized Out-of-Distribution Detection: A Survey
  21 Oct 2021 4 repositories listed
  In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.
• Probabilistic Autoencoder
  9 Jun 2020 4 repositories listed Syntology ran 1 of 13 samples · 12 unverified
  The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks.
• Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
  28 Jun 2019 4 repositories listed Syntology ran 5 of 12 samples · 7 unverified
  Self-supervision provides effective representations for downstream tasks without requiring labels.
• PyOD: A Python Toolbox for Scalable Outlier Detection
  6 Jan 2019 4 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
• Geometry- and Accuracy-Preserving Random Forest Proximities
  29 Jan 2022 3 repositories listed
  Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning.
• SSD: A Unified Framework for Self-Supervised Outlier Detection
  22 Mar 2021 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin.
• COPOD: Copula-Based Outlier Detection
  20 Sep 2020 3 repositories listed
  In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets…
• AdaLAM: Revisiting Handcrafted Outlier Detection
  7 Jun 2020 3 repositories listed Syntology ran 0 of 19 samples · 19 unverified
  Local feature matching is a critical component of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization.
• An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
  14 Aug 2018 3 repositories listed
  This article starts with a categorization of the various methods.
• Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
  13 Jun 2018 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can…
• Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
  29 Oct 2024 2 repositories listed
  In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric.
• Rethinking Unsupervised Outlier Detection via Multiple Thresholding
  7 Jul 2024 2 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  In the realm of unsupervised image outlier detection, assigning outlier scores holds greater significance than its subsequent task: thresholding for predicting labels.
• Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces
  24 Nov 2023 2 repositories listed
  Therefore, state-of-the-art supervised Concept-based eXplainable Artificial Intelligence (C-XAI) methods associate user-defined concepts like ``car'' each with a single vector in the DNN latent space (concept embedding…
• LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data
  19 Sep 2023 2 repositories listed
  We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.
• Computationally Assisted Quality Control for Public Health Data Streams
  29 Jun 2023 2 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams.
• SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection
  16 Feb 2023 2 repositories listed
  Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with…
• BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
  21 Jun 2022 2 repositories listed
  To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
• DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
  7 Jun 2022 2 repositories listed
  Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines.
• ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
  2 Jan 2022 2 repositories listed
  To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that…
• TOD: GPU-accelerated Outlier Detection via Tensor Operations
  26 Oct 2021 2 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.
• Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
  3 Oct 2021 2 repositories listed Syntology ran 6 of 22 samples · 16 unverified
  We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees.
• Autoencoding Under Normalization Constraints
  12 May 2021 2 repositories listed Syntology ran 7 of 9 samples · 2 unverified
  The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood.
• A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
  27 Aug 2020 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.

Syntology lines on 18 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.