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I recently learnt about the anamoly detection using autoencoders(specifically denoisinng autoencoders).To train the autoencoders do we need a data having some pattern? or is there some way to train in abnormal data ?Also how we decide that the data is suitable for training autoencoder model.

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  • $\begingroup$ Alternatively you could use something like Isolation Forest which allows you to include anomalous data in training. Check out sklearn's implementation and the contamination parameter. $\endgroup$ – Simon Larsson Feb 13 '20 at 11:00
  • $\begingroup$ I think the variational autencoder is superior to the denoising autoencoder, specifically in the ability to generate new data. $\endgroup$ – Victor Ng Mar 14 '20 at 13:56
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You need normal data to train on. If you have abnormal instances also, those should be excluded from the training set. Having access to labeled abnormal/normal data is very useful for the validation and testset. Anything that differs from the normal data (as learned by the autoencoder) is considered an anomaly.

If you have a lot of labeled abnormal and normal data, then you can consider using binary classification instead.

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