Ying, H, Wang, H orcid.org/0000-0002-2281-5679, Shao, T et al. (2 more authors) (2022) Unsupervised Image Generation with Infinite Generative Adversarial Networks. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021), 10-17 Oct 2021, Montreal, QC, Canada. , pp. 14264-14273. ISBN 978-1-6654-2813-2
Abstract
Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision. Generative Adversarial Networks (GANs) as an implicit approach have achieved great successes in this direction and therefore been employed widely. However, GANs are known to suffer from issues such as mode collapse, non-structured latent space, being unable to compute likelihoods, etc. In this paper, we propose a new unsupervised non-parametric method named mixture of infinite conditional GANs or MIC-GANs, to tackle several GAN issues together, aiming for image generation with parsimonious prior knowledge. Through comprehensive evaluations across different datasets, we show that MIC-GANs are effective in structuring the latent space and avoiding mode collapse, and outperform state-of-the-art methods. MICGANs are adaptive, versatile, and robust. They offer a promising solution to several well-known GAN issues. Code available:github.com/yinghdb/MICGANs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Image and video synthesis; Machine learning architectures and formulations |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Aug 2021 12:49 |
Last Modified: | 12 Oct 2023 11:06 |
Status: | Published |
Identification Number: | 10.1109/ICCV48922.2021.01402 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177274 |