A Survey of Missing Data Imputation Using Generative Adversarial Networks

Recently, many deep learning models for missing data imputation have been studied. One of the most popular models is Generative Adversarial Networks (GANs), which generate plausible fake data through adversarial training. In this paper, we take a look at the architecture, objective of a generator and a discriminator, training method and loss function. After that, we can see what improvements have been made to each model. Moreover, we can easily compare several GAN-based models for missing data imputation.

[1]  Mihaela van der Schaar,et al.  GAIN: Missing Data Imputation using Generative Adversarial Nets , 2018, ICML.

[2]  Thomas S. Huang,et al.  Generative Image Inpainting with Contextual Attention , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Thomas S. Huang,et al.  Free-Form Image Inpainting With Gated Convolution , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[4]  Radu State,et al.  Improving Missing Data Imputation with Deep Generative Models , 2019, ArXiv.

[5]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[6]  Jong Chul Ye,et al.  CollaGAN: Collaborative GAN for Missing Image Data Imputation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Hiroshi Ishikawa,et al.  Globally and locally consistent image completion , 2017, ACM Trans. Graph..

[8]  Bo Jiang,et al.  MisGAN: Learning from Incomplete Data with Generative Adversarial Networks , 2019, ICLR.