Minqi824/ADGym
Official Implement of "ADGym: Design Choices for Deep Anomaly Detection", NeurIPS 2023
This tool helps data scientists and machine learning engineers working with tabular datasets to identify anomalies or outliers more effectively. It automates the process of selecting the best combination of techniques for data preparation, model architecture, and training, taking the guesswork out of building anomaly detection models. You input your tabular data, and it helps benchmark and automatically select optimal anomaly detection settings.
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Use this if you need to build robust deep anomaly detection models for tabular data and want to efficiently explore and select the best design choices without manual trial and error.
Not ideal if your anomaly detection task involves non-tabular data types like images, text, or time series, as it is primarily designed for tabular datasets.
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34
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Language
Python
License
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Category
Last pushed
Aug 23, 2023
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