Presentation | 2017-10-12 [Tutorial Lecture] Families of GANs Tomohiro Takahashi, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Generative Adversarial Networks(GANs) have recently gained popularity due to their ability to synthesize images which are quite similar to target images. As a result, these are used in various applications such as image domain transfer, generating unseen images etc. However, there are several challenges in training a GAN model like mode collapse, blurred outputs etc. In this tutorial, we would address these issues and present solutions for the same. This tutorial will consists of 1. Introduction to how GANs work, 2. Modified objectives for GANsand 3. Applications of GANs in image generation. As both the applications and research on GANs is growing, this tutorial will provide researchers an introduction and common challenges with GAN models. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Deep Learning / Generative Adversarial Nets |
Paper # | PRMU2017-80 |
Date of Issue | 2017-10-05 (PRMU) |
Conference Information | |
Committee | PRMU |
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Conference Date | 2017/10/12(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
Chair | Shinichi Sato(NII) |
Vice Chair | Hironobu Fujiyoshi(Chubu Univ.) / Yoshihisa Ijiri(Omron) |
Secretary | Hironobu Fujiyoshi(AIST) / Yoshihisa Ijiri(NAIST) |
Assistant | Masato Ishii(NEC) / Yusuke Sugano(Osaka Univ.) |
Paper Information | |
Registration To | Technical Committee on Pattern Recognition and Media Understanding |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | [Tutorial Lecture] Families of GANs |
Sub Title (in English) | |
Keyword(1) | Deep Learning |
Keyword(2) | Generative Adversarial Nets |
1st Author's Name | Tomohiro Takahashi |
1st Author's Affiliation | ABEJA, Inc.(ABEJA) |
Date | 2017-10-12 |
Paper # | PRMU2017-80 |
Volume (vol) | vol.117 |
Number (no) | PRMU-238 |
Page | pp.pp.95-100(PRMU), |
#Pages | 6 |
Date of Issue | 2017-10-05 (PRMU) |