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.
authors: Mengye Ren Renjie Liao Ethan Fetaya Richard S. Zemel
hIndex: Richard S. Zemel 68