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pokaxpoka / deep_Mahalanobis_detector

Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".
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How to calculate tied covariance matrix #19

Open lumliolum opened 1 year ago

lumliolum commented 1 year ago

As per the formulae given in the paper

image

which is equalivalent to calculate the covaraince matrix for each class and then take the weighted average to get the tied covariance matrix. But in the code, https://github.com/pokaxpoka/deep_Mahalanobis_detector/blob/90c2105e78c6f76a2801fc4c1cb1b84f4ff9af63/lib_generation.py#L107-L120

you are using sklearn.covariance.EmpiricalCovariance for all of the data (see line 117 X) but as per formulae you calculate the covariance for each class and then take the average. So I feel that we should apply sklearn.covariance.EmpiricalCovariance per class and then take the sum.

Thanks,