hendrycks/outlier-exposure
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
This helps deep learning models better identify unusual or unexpected data points, often called anomalies. You provide your existing dataset along with an additional dataset of known 'outlier' examples, and the system improves your model's ability to spot anomalies it hasn't seen before. This is for machine learning engineers or researchers working with deep learning models who need to improve the reliability of anomaly detection.
575 stars. No commits in the last 6 months.
Use this if you need to significantly boost the accuracy of your deep learning model in recognizing rare or anomalous events without needing to tune for each new type of anomaly.
Not ideal if you don't have access to a large, diverse dataset of out-of-distribution examples to train with or if you are not using deep learning models.
Stars
575
Forks
107
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 09, 2021
Commits (30d)
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