Semi-supervised Anomaly Detection
28 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-supervised Anomaly Detection
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Libraries
Use these libraries to find Semi-supervised Anomaly Detection models and implementations
MVTec LOCO AD
UBI-Fights
Latest papers with no code
Machine learning-based identification of Gaia astrometric exoplanet orbits
The third Gaia data release (DR3) contains $\sim$170 000 astrometric orbit solutions of two-body systems located within $\sim$500 pc of the Sun.
Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
In GBRBM-based anomaly detection, normal and anomalous data are classified based on a score that is identical to an energy function of the marginal GBRBM.
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion
The Health Index (HI) is crucial for evaluating system health, aiding tasks like anomaly detection and predicting remaining useful life for systems demanding high safety and reliability.
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market
Fraud detection is overwhelmingly associated with the greater field of anomaly detection, which is usually performed via unsupervised learning techniques because of the lack of labeled data needed for supervised learning.
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets
The CBLOF method was also able to obtain a F1-Score of 0. 8, whereas the KNN produced a 0. 78.
Future Video Prediction from a Single Frame for Video Anomaly Detection
Inspired by the abilities of the future frame prediction proxy-task, we introduce the task of future video prediction from a single frame, as a novel proxy-task for video anomaly detection.
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling
Second, we use the normal and the synthetic samples as input to our model.
Reconstruction Error-based Anomaly Detection with Few Outlying Examples
It consists in training an Autoencoder to reconstruct a set of examples deemed to represent the normality and then to point out as anomalies those data that show a sufficiently large reconstruction error.
AGAD: Adversarial Generative Anomaly Detection
In order to address the lack of abnormal data for robust anomaly detection, we propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm that learns to detect anomalies by generating \textit{contextual adversarial information} from the massive normal examples.
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection
To solve this problem, we propose a domain adaptation method for SSAD where no anomalous data are available for the target domain.
