Dist-GAN: An Improved GAN Using Distance Constraints

  • Ngoc-Trung TranEmail author
  • Tuan-Anh Bui
  • Ngai-Man Cheung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)


We introduce effective training algorithms for Generative Adversarial Networks (GAN) to alleviate mode collapse and gradient vanishing. In our system, we constrain the generator by an Autoencoder (AE). We propose a formulation to consider the reconstructed samples from AE as “real” samples for the discriminator. This couples the convergence of the AE with that of the discriminator, effectively slowing down the convergence of discriminator and reducing gradient vanishing. Importantly, we propose two novel distance constraints to improve the generator. First, we propose a latent-data distance constraint to enforce compatibility between the latent sample distances and the corresponding data sample distances. We use this constraint to explicitly prevent the generator from mode collapse. Second, we propose a discriminator-score distance constraint to align the distribution of the generated samples with that of the real samples through the discriminator score. We use this constraint to guide the generator to synthesize samples that resemble the real ones. Our proposed GAN using these distance constraints, namely Dist-GAN, can achieve better results than state-of-the-art methods across benchmark datasets: synthetic, MNIST, MNIST-1K, CelebA, CIFAR-10 and STL-10 datasets. Our code is published here ( for research.


Generative Adversarial Networks Image generation Distance constraints Autoencoders 



This work was supported by both ST Electronics and the National Research Foundation(NRF), Prime Minister’s Office, Singapore under Corporate Laboratory @ University Scheme (Programme Title: STEE Infosec - SUTD Corporate Laboratory).

Supplementary material

474202_1_En_23_MOESM1_ESM.pdf (446 kb)
Supplementary material 1 (pdf 446 KB)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ST Electronics - SUTD Cyber Security LaboratorySingapore University of Technology and DesignSingaporeSingapore

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