  Deep neural networks have shown superior performance in many regimes to
remember familiar patterns with large amounts of data. However, the standard
supervised deep learning paradigm is still limited when facing the need to
learn new concepts efficiently from scarce data. In this paper, we present a
memory-augmented neural network which is motivated by the process of human
concept learning. The training procedure, imitating the concept formation
course of human, learns how to distinguish samples from different classes and
aggregate samples of the same kind. In order to better utilize the advantages
originated from the human behavior, we propose a sequential process, during
which the network should decide how to remember each sample at every step. In
this sequential process, a stable and interactive memory serves as an important
module. We validate our model in some typical one-shot learning tasks and also
an exploratory outlier detection problem. In all the experiments, our model
gets highly competitive to reach or outperform those strong baselines.
