Learning from Few Samples with Memory Network

Neural Networks (NN) have achieved great success in pattern recognition and machine learning. However, the success of NNs usually relies on a sufficiently large number of samples. When fed with limited data, NN's performance may be degraded significantly. In this paper, we introduce a novel neural network called Memory Network, which can learn better from limited data. Taking advantages of the memory from previous samples, the new model could achieve remarkable performance improvement on limited data. We demonstrate the memory network in Multi-Layer Perceptron (MLP). However, it keeps straightforward to extend our idea to other neural networks, e.g., Convolutional Neural Networks (CNN). We detail the network structure, present the training algorithm, and conduct a series of experiments to validate the proposed framework. Experimental results show that our model outperforms the traditional MLP and other competitive algorithms in two real data sets.