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Could you explain in a bit more detail how you would integrate an SVM layer into a DNN? The kernel matrix depends on all samples, while at training time you would only have access to those in the minibatch.

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The simplest is to pop it on the top. Run you DNN to reduce your input down to a nicer cleaner smaller dimensional output, then plop an SVM on top for classification.

Seems like in that case you would train both models separately on different cost functions. By phrasing it as a layer I was expecting both the SVM and the DNN could be trained simultaneously.

Unless things have changed, one of the key benefits of DNNs was that you trained them layer by layer.

You also want to be able to train the DNN on your unlabelled data and the SVM on your much smaller labelled set.

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