Autoencoder anamoly detection

Question

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.

Answer 1

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.