lukasruff/Deep-SAD-PyTorch
A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
This project helps data scientists or machine learning engineers identify rare, unusual, or suspicious data points in large datasets. It takes a mix of labeled (known normal or abnormal) and unlabeled data to build a model that can flag anomalies. The output is a method to detect these unusual patterns, even with very little initial labeled information.
351 stars. No commits in the last 6 months.
Use this if you need to detect anomalies in complex, high-dimensional datasets and have a small amount of labeled examples (both normal and anomalous) to guide the detection process.
Not ideal if you have no labeled data at all, or if your anomaly detection needs are simple and can be met with traditional, less computationally intensive methods.
Stars
351
Forks
100
Language
Python
License
MIT
Category
Last pushed
Nov 22, 2022
Commits (30d)
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