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As an example application, we consider a neural network that has been trained to identify people and organizations in online news articles.We show that it is possible to transfer that network and its learned parameters into a new network trained to identify an additional type of named entity — locations.But what happens if you need to add a new class to your classifier — if, say, someone releases a new type of automated household appliance that your smart-home system needs to be able to control?
The first transfer-learning method we examine is to simply expand the size of the trained network’s output layer and the layers immediately beneath it, to accommodate the addition of the new class. We then compare this approach to the one that uses the neural adapter.The output of the neural adapter joins the data flow of the new network just below the final layer (the CRF layer in one case, the final classification layer in the other).For both initial architectures and both transfer-learning methods, we considered the case in which we allowed only the weights of the top few layers to vary during retraining and the case in which we allowed the weights of the entire network to vary.Alexa scientists and engineers have poured a great deal of effort into Alexa’s core functionality, but through the Alexa Skills Kit, we’ve also enabled third-party developers to build their own Alexa skills — 70,000 and counting.The type of adaptation — or “transfer learning” — that we study in the new paper would make it possible for third-party developers to make direct use of our in-house systems without requiring access to in-house training data.
Many of today’s most popular AI systems are, at their core, classifiers.