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A Network Framework for Small-Sample Learning
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DOI：10.1109/TNNLS.2019.2951803.png)
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Abstract
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Small-sample learning involves training a neural network on a small-sample data set. An expansion of the training set is a common way to improve the performance of neural networks in small-sample learning tasks. However, improper constraints in expanding training data will reduce the performance of the neural networks. In this article, we present certain conditions for incorporation of additional training data. According to these conditions, we propose a neural network framework for self-training using self-generated data called small-sample learning network (SSLN). The SSLN consists of two parts: the expression learning network and the sample recall generative network, both of which are constructed based on restricted Boltzmann machine (RBM). We show that this SSLN can converge as well as the RBM. Moreover, the experiment results on MNIST Digit, SVHN, CIFAR10, and STL-10 data sets reveal the superiority of the SSLN over other models.
Keywords:
Neural networks
Training
Training data
Task analysis
Data models
Deep learning
Semisupervised learning
Expression learning network
generative network
restricted Boltzmann machine (RBM)
small-sample learning
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