Updating network architecture

Every aspect of the network architecture and infrastructure must support future growth and expansion without unnecessary upgrades.

Typically, those nodes are arranged into layers, and the output of each layer passes to the layer above it.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.With today’s commercial AI systems, many of which were trained on millions of examples, this is a laborious process.This week, at the 33rd conference of the Association for the Advancement of Artificial Intelligence (AAAI), my colleague Lingzhen Chen from the University of Trento and I are presenting a paper on techniques for updating a classifier using only training data for the new class.

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