Title: Incremental Few-Shot Learning with Attention Attractor Networks. Abstract: Machine learning classifiers are often trained to recognize a set of pre-defined classes. However,
in many real applications, it is often desirable to have the flexibility of learning additional
concepts, without re-training on the full training set. This paper addresses this problem,
incremental few-shot learning, where a regular classification network has already been trained to
recognize a set of base classes; and several extra novel classes are being considered, each with
only a few labeled examples. After learning the novel classes, the model is then evaluated on the
overall performance of both base and novel classes. To this end, we propose a meta-learning model,
the Attention Attractor Network, which regularizes the learning of novel classes. In each episode,
we train a set of new weights to recognize novel classes until they converge, and we show that the
technique of recurrent back-propagation can back-propagate through the optimization process and
facilitate the learning of the attractor network regularizer. We demonstrate that the learned
attractor network can recognize novel classes while remembering old classes without the need to
review the original training set, outperforming baselines that do not rely on an iterative
optimization process.