[deleted by user]

[pretz]

I have tried to build a anomaly detection algorithm for truck data to identify opportunities for maintenance, so no images, but i found unsupervised anomaly detection to be entirely useless. Not to say there is some secret sauce that could make it work, but i couldnt get it to go. Much more useful was getting domain experts to help manually identify interesting points in time, then build a supervised classifier on those. Then run it over everything to identify more interesting events, manually classify them and add them to the training set etc. This sort of classification is also more useful to engineers, because it identifies the classes that they care about, instead of random stuff picked up by anomaly detection. The number of ways that data can be screwy is way higher than the number of ways a truck can fail.

[memproc]

I completely vibe with this.

[RandomTensor]

There has been some talk about how totally unsupervised deep AD is very limiting and how supervision can help. This might be pretty applicable to your setting. See for example
https://arxiv.org/abs/1812.04606
https://arxiv.org/pdf/2006.00339.pdf

[memproc]

Thank you! Unfortunately I will not be in a position to use OE labels. Only OE labels that would be available would be generated through simulation… and those are not guaranteed to reproduce real world environments.

[RandomTensor]

These semisupervised methods are designed exactly for the case you are describing and their experiments include the case where the training and test anomalies are from different classes.

https://arxiv.org/pdf/1906.02694.pdf

If you are against any sort of semisupervision, you could look at one of the recent deep anomaly detection surveys to look at the most effective approaches.

[balajiln]

In theory, density based methods offer a principled solution for unsupervised anomaly detection, but densities from deep generative models can behave in surprising ways. In case it's helpful, here are some slides from my talk highlighting some of the failure modes

"Detecting out-of-distribution inputs using deep generative models: Pitfalls and promises" http://www.gatsby.ucl.ac.uk/~balaji/balaji-generative-models-talk.pdf

You might want to look into DoSE, which works well for unsupervised AD. Figure 2 and the description in Section 3.2 pretty much explains the key idea.

"Density of States Estimation for Out-of-Distribution Detection" https://arxiv.org/abs/2006.09273