A Network Framework for Small-Sample Learning
| Autor: | Dongbo Liu, Dongdong Chen, Zhenan He, Jiancheng Lv |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Restricted Boltzmann machine
Training set Artificial neural network Computer Networks and Communications business.industry Computer science Deep learning Sample (statistics) 02 engineering and technology Machine learning computer.software_genre Expression (mathematics) Computer Science Applications Data modeling Data set Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Software MNIST database |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 31:4049-4062 |
| ISSN: | 2162-2388 2162-237X |
| DOI: | 10.1109/tnnls.2019.2951803 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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