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I was just wondering if you managed to reproduce the results in Zong, Bo, et al. "Deep autoencoding gaussian mixture model for unsupervised anomaly detection." International conference on learning representations. 2018.
Hi @jonomon, there can be some subtle differences in the way precision/recall are computed, the way the detection threshold is chosen, and how the model handles point data (which the Thyroid dataset is) vs time series data. Before anything else, you should try to use PointwisePrecision, PointwiseRecall, and PointwiseF1 as your evaluation metrics, as the default ones are specialized for time series data. If this doesn't resolve the issue, @yangwenzhuo08 can you answer any further questions?
Hello,
Thank you for the nice library!
I was just wondering if you managed to reproduce the results in Zong, Bo, et al. "Deep autoencoding gaussian mixture model for unsupervised anomaly detection." International conference on learning representations. 2018.
I used the following configuration:
and only managed to get the following results on the Thyroid dataset (.mat obtained from http://odds.cs.stonybrook.edu):
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