Abstract
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.
Original language | English |
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Title of host publication | 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 454-456 |
Number of pages | 3 |
ISBN (Electronic) | 9781728149851 |
DOIs | |
Publication status | Published - 2020 Feb 1 |
Event | 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan Duration: 2020 Feb 19 → 2020 Feb 21 |
Publication series
Name | 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 |
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Conference
Conference | 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 |
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Country/Territory | Japan |
City | Fukuoka |
Period | 20/2/19 → 20/2/21 |
Bibliographical note
Funding Information:The correspondence should be addressed to jseok14@korea.ac.kr. This work was supported by grants from the National Research Foundation of Korea (NRF-2017R1C1B2002850, NRF-2019R1A2C1084778)
Publisher Copyright:
© 2020 IEEE.
Keywords
- Adversarial training
- Discriminator
- Generative Adversarial Networks (GANs)
- Generator
- Missing data imputation
ASJC Scopus subject areas
- Information Systems and Management
- Artificial Intelligence
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Information Systems
- Signal Processing