Updating network architecture
In particular, we found that the most effective technique is to keep the original classifier; pass its output through a separate network, which we call a neural adapter; and use the output of the neural adapter as an additional input to a second, parallel classifier, which is trained on just the data for the new class.
The adapter and the new classifier are trained together, and at run time, the same input passes to both classifiers.
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?
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Every aspect of a business depends on the phone and data communication that is supported by the network for internal and external interaction.