Anomaly Detection
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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Subtasks
Greatest papers with code
Temporal Cycle-Consistency Learning
CVPR 2019 • google-research/google-research
We introduce a self-supervised representation learning method based on the task of temporal alignment between videos.
PyOD: A Python Toolbox for Scalable Outlier Detection
• yzhao062/pyod
• 
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of vertices in a network.
#3 best model for
Node Classification
on Wikipedia
ANOMALY DETECTION DOCUMENT CLASSIFICATION LANGUAGE MODELLING NODE CLASSIFICATION
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection
We present a novel algorithm for anomaly detection on very large datasets and data streams.
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
GluonTS: Probabilistic Time Series Models in Python
• awslabs/gluon-ts
• 
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
SOTA for Time Series on Bitcoin-Alpha
ANOMALY DETECTION TIME SERIES TIME SERIES FORECASTING TIME SERIES PREDICTION
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
• chickenbestlover/RNN-Time-series-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.
#2 best model for
Time Series Classification
on Physionet 2017 Atrial Fibrillation
ANOMALY DETECTION OUTLIER DETECTION TIME SERIES TIME SERIES CLASSIFICATION
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability
In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.
GLAD: GLocalized Anomaly Detection via Active Feature Space Suppression
We propose an algorithm called GLAD (GLocalized Anomaly Detection) that allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback.