ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficac...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 12; s. 9733 - 9740
Hlavní autoři:	Yan, Caixia, Chang, Xiaojun, Li, Zhihui, Guan, Weili, Ge, Zongyuan, Zhu, Lei, Zheng, Qinghua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Computer architecture Datasets Differentiable architecture search Generative adversarial networks Generators Optimization Search methods Source code Task analysis Testing Training Zero-shot learning
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS .
AbstractList	In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS . In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS.In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS.
Author	Zhu, Lei Chang, Xiaojun Zheng, Qinghua Yan, Caixia Ge, Zongyuan Li, Zhihui Guan, Weili
Author_xml	– sequence: 1 givenname: Caixia orcidid: 0000-0003-2595-0398 surname: Yan fullname: Yan, Caixia email: yancaixia@stu.xjtu.edu.cn organization: Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China – sequence: 2 givenname: Xiaojun orcidid: 0000-0002-7778-8807 surname: Chang fullname: Chang, Xiaojun email: cxj273@gmail.com organization: School of Computing Technologies, RMIT University, Melbourne, VIC, Australia – sequence: 3 givenname: Zhihui orcidid: 0000-0001-9642-8009 surname: Li fullname: Li, Zhihui email: zhihuilics@gmail.com organization: Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China – sequence: 4 givenname: Weili orcidid: 0000-0002-5658-5509 surname: Guan fullname: Guan, Weili email: honeyguan@gmail.com organization: Faculty of Information Technology, Monash University, Melbourne, VIC, Australia – sequence: 5 givenname: Zongyuan orcidid: 0000-0002-5880-8673 surname: Ge fullname: Ge, Zongyuan email: Zongyuan.Ge@monash.edu organization: Faculty of Information Technology, Monash University, Melbourne, VIC, Australia – sequence: 6 givenname: Lei orcidid: 0000-0002-2993-7142 surname: Zhu fullname: Zhu, Lei email: leizhu0608@gmail.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan, China – sequence: 7 givenname: Qinghua surname: Zheng fullname: Zheng, Qinghua email: qhzheng@mail.xjtu.edu.cn organization: Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China
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Snippet	In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of...
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SubjectTerms	Computer architecture Datasets Differentiable architecture search Generative adversarial networks Generators Optimization Search methods Source code Task analysis Testing Training Zero-shot learning
Title	ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning
URI	https://ieeexplore.ieee.org/document/9612044 https://www.proquest.com/docview/2734385793 https://www.proquest.com/docview/2597498126
Volume	44
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