An Integrated Framework Based on GAN and RBI for Learning with Insufficient Datasets

• Published in: Symmetry (Basel) Vol. 14; no. 2; p. 339
• Main Authors: Lin, Yao-San, Lin, Liang-Sian, Chen, Chih-Ching
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Bayesian analysis; Cognitive tasks; data augmentation; Datasets; Generative adversarial networks; Literature reviews; Machine learning; mega-trend-diffusion; Population; Product development; robust Bayesian inference; Robustness; small sample learning; Statistical inference; virtual sample generation; WGAN
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym14020339

Generative adversarial networks are known as being capable of outputting data that can imitate the input well. This characteristic has led the previous research to propose the WGAN_MTD model, which joins the common version of Generative Adversarial Networks and Mega-Trend-Diffusion methods. To prevent the data-driven model from becoming susceptible to small datasets with insufficient information, we introduced a robust Bayesian inference to the process of virtual sample generation based on the previous version and proposed its refined version, WGAN_MTD2. The new version allows users to append subjective information to the contaminated estimation of the unknown population, at a certain level. It helps Mega-Trend-Diffusion methods take into account not only the information from original small datasets but also the user’s subjective information when generating virtual samples. The flexible model will not be subject to the information from the present datasets. To verify the performance and confirm whether a robust Bayesian inference benefits the effective generation of virtual samples, we applied the proposed model to the learning task with three open data and conducted corresponding experiments for the significance tests. As the experimental study revealed, the integrated framework based on GAN and RBI, WGAN_MTD2, can perform better and lead to higher learning accuracies than the previous one. The results also confirm that a robust Bayesian inference can improve the information capturing from insufficient datasets.