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Semi-supervised Multi-category Classification with Generative Adversarial Networks

  • Reshma RastogiEmail author
  • Ritesh Gangnani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)

Abstract

For training robust deep neural architectures to generate complex samples across varied domains, Generative Adversarial Networks (GANs) have shown promising performance in recent years. In previous works, the effectiveness of GANs in transforming images from the laseled source domain to the unlabeled target domain has shown high potential. In this paper, we outline a generalized semi-supervised learning framework where proposed ‘Semi-supervised Multi-category Classification with Generative Adversarial Networks (SMC-GAN)’ model, first, maps the data in the source domain to target domain to generate target-like source images and, then, learns to discriminate the target domain data using semi-supervised classifier. Extensive experimental evaluations on standard cross-domain datasets show that the proposed model is an efficient classifier and allows faster convergence than a conventional GAN approach for digit classification tasks.

Keywords

Domain adaptation Adversarial learning GAN Semi-supervised learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.South Asian UniversityNew DelhiIndia

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