Anomaly Detection
1196 papers with code • 64 benchmarks • 92 datasets
Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.
[Image source]: GAN-based Anomaly Detection in Imbalance Problems
Libraries
Use these libraries to find Anomaly Detection models and implementationsDatasets
Subtasks
- Unsupervised Anomaly Detection
- One-Class Classification
- Supervised Anomaly Detection
- Anomaly Detection In Surveillance Videos
- Anomaly Detection In Surveillance Videos
- Graph Anomaly Detection
- Image Manipulation Detection
- Weakly-supervised Anomaly Detection
- Abnormal Event Detection In Video
- Self-Supervised Anomaly Detection
- 3D Anomaly Detection
- 3D Anomaly Detection and Segmentation
- RGB+3D Anomaly Detection and Segmentation
- Contextual Anomaly Detection
- Depth Anomaly Detection and Segmentation
- Group Anomaly Detection
- RGB+Depth Anomaly Detection and Segmentation
- Damaged Tissue Detection
- Unsupervised Anomaly Detection In Sound
- 3D Anomaly Segmentation
- Depth Anomaly Segmentation
- 3D + RGB Anomaly Segmentation
- Depth + RGB Anomaly Segmentation
- Depth + RGB Anomaly Detection
- 3D + RGB Anomaly Detection
- DepthAnomaly Detection
Most implemented papers
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
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 other class (abnormal).
Deep Anomaly Detection with Outlier Exposure
We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
Real-world Anomaly Detection in Surveillance Videos
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 training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk.
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
Efficient GAN-Based Anomaly Detection
However, few works have explored the use of GANs for the anomaly detection task.
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS).