lukasruff/Deep-SVDD-PyTorch
A PyTorch implementation of the Deep SVDD anomaly detection method
This project helps identify unusual or 'anomalous' items within a dataset, like finding a counterfeit bill in a stack of legitimate currency or a faulty sensor reading among normal ones. It takes a collection of data samples, such as images or numerical readings, and outputs an 'anomaly score' for each, indicating how unusual it is. This is ideal for quality control specialists, fraud detection analysts, or anyone who needs to automatically flag rare or out-of-place data points.
779 stars. No commits in the last 6 months.
Use this if you need to detect anomalies or outliers in your datasets, especially with image data, by training a model solely on 'normal' examples.
Not ideal if you have a dataset with many known types of anomalies and want to classify them into specific categories, rather than just identifying them as unusual.
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779
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Language
Python
License
MIT
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Last pushed
Dec 08, 2022
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