Anomaly Detection - Groups

UCSD Peds1 and UCSD Peds2

Anomaly detection in crowded scenes

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UCSD dataset

The UCSD dataset is a benchmark for the analysis of abnormal detection and localization in crowded scenes.

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Wood

A common study area in anomaly identiﬁcation is industrial images anomaly detection based on texture background.

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Tile

A common study area in anomaly identiﬁcation is industrial images anomaly detection based on texture background.

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Leather

A common study area in anomaly identiﬁcation is industrial images anomaly detection based on texture background.

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Carpet

A common study area in anomaly identiﬁcation is industrial images anomaly detection based on texture background.

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Twitch Plays Pokemon

The dataset is a collection of user messages, commands, time, and user information from the Twitch Plays Pokemon game.

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Custom Carpet Dataset

A custom dataset of four unique types of carpet textures was created to thoroughly test state-of-the-art unsupervised detection models.

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SMD

This dataset is used for time-series anomaly detection. It contains multivariate time-series data from various sources, including industrial machines and sensors.

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KolektorSDD2

The KolektorSDD2 dataset is a dataset containing 3,335 color images of defective production items, with defects localized using pixel-wise masks.

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N-BaIoT dataset

The N-BaIoT dataset is a large dataset of network traffic traces from various IoT devices, including botnets.

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Spot-the-difference self-supervised pre-training for anomaly detection and se...

The Spot-the-difference self-supervised pre-training for anomaly detection and segmentation dataset.

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Deep Graph Learning for Anomalous Citation Detection

Anomalous citation detection in academic citation networks

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LANL dataset

The LANL dataset consists of over one billion log lines collected over 58 consecutive days. The logs contain anonymized process, network flow, DNS, and authentication information.

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UGRansome

The UGRansome dataset is a novel and comprehensive anomaly detection dataset designed to detect unknown network attacks, including zero-day threats.

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SARAS

The SARAS dataset is a dataset for anomaly detection in robotic-assisted surgery. It contains 18 videos of radical prostatectomy procedures performed on artificial anatomies.