Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning

Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SSL. Previous work in applying GANs to SSL are classified into pseudo-labeling/classification, encoder-based, TripleGAN-based, two GAN, manifold regularization, and stacked discriminator approaches. A quantitative and qualitative analysis of the various approaches is presented. The R3-CGAN architecture is identified as the GAN architecture with state-of-the-art results. Given the recent success of non-GAN-based approaches for SSL, future research opportunities involving the adaptation of elements of SSL into GAN-based implementations are also identified.

Automated summary

This paper presents a survey method using Generative Adversarial Networks (GANs) for semi-supervised learning (SSL), analyzing and identifying the state-of-the-art GAN architecture for SSL. It also identifies future research opportunities involving the adaptation of SSL elements into GAN-based implementations.