Revisiting improvedgan with metric learning for semi-supervised learning

Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Semi-supervised Learning (SSL) is a classical problem where a model needs to solve classification as it is trained on a partially labeled train data. After the introduction of generative adversarial network (GAN) and its success, the model has been modified to be applicable to SSL. ImprovedGAN as a representative model for GAN-based SSL, it showed promising performance on the SSL problem. However, the inner mechanism of this model has been only partially revealed. In this work, we revisit ImprovedGAN with a fresh look on it based on metric learning. In particular, we interpret ImprovedGAN by general pair weighting, a recent framework in metric learning. Based on this interpretation, we derive two theoretical properties of ImprovedGAN: (i) its discriminator learns to make confident predictions over real samples, (ii) the adversarial interaction in ImprovedGAN constrains the discriminator to decrease the angles between the features of real samples and class weight vectors. The two properties suggest that the adversarial interaction induces the class-wise cluster separation of the features as experimentally verified. Motivated by the findings, we propose a variant of ImprovedGAN, called Intensified ImprovedGAN, where its cluster separation characteristic is improved by two proposed techniques: (a) the unsupervised discriminator loss is scaled up and (b) the generated batch size is enlarged. As a result, I2GAN produces better class-wise cluster separation and, hence, generalization. Extensive experiments on the widely known benchmark data sets verify the effectiveness of our proposed method, showing that its performance is better than or comparable to other GAN based SSL models.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728188089
Publication statusPublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: 2021 Jan 102021 Jan 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference25th International Conference on Pattern Recognition, ICPR 2020
CityVirtual, Milan

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NO. NRF-2019R1A2C1003306), and by NVIDIA GPU grant program.

Publisher Copyright:
© 2020 IEEE

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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