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题名Unsupervised meta-learning via spherical latent representations and dual VAE-GAN
作者
发表日期2023-10-01
发表期刊Applied Intelligence
ISSN/eISSN0924-669X
卷号53期号:19页码:22775-22788
摘要

Unsupervised learning and meta-learning share a common goal of enhancing learning efficiency compared to starting from scratch. However, meta-learning methods are predominantly employed in supervised settings, where acquiring labels for meta-training is costly and new tasks are limited to a predefined distribution of training tasks. In this paper, we introduce a novel unsupervised meta-learning framework that leverages spherical latent representations defined on a unit hypersphere. Unlike the state-of-the-art unsupervised meta-learning approach that assumes a Gaussian mixture prior over latent representations, we utilize a von Mises-Fisher mixture model for constructing the latent space. This alternative formulation leads to a more stable optimization process and improved performance. To enhance the generative capability of our model, we unify the variational autoencoder (VAE) and the generative adversarial network (GAN) within our unsupervised meta-learning framework. Moreover, we propose a dual VAE-GAN framework to impose a reconstruction constraint on both the latent representations and their corresponding transformed versions, thereby yielding more representative and discriminative representations. The efficacy of our proposed unsupervised meta-learning framework is demonstrated through extensive comparisons with existing methods on diverse benchmark datasets.

关键词GAN Meta-learning Unsupervised learning VAE Von Mises-Fisher
DOI10.1007/s10489-023-04760-9
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收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:001021395900002
Scopus入藏号2-s2.0-85163776642
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/10896
专题理工科技学院
通讯作者Fan, Wentao
作者单位
1.Department of Computer Science,Beijing Normal University-Hong Kong Baptist University United International College (BNU-HKBU UIC),Zhuhai,Guangdong,China
2.Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United International College,Zhuhai,China
3.Department of Computer Science and Technology,Huaqiao University,Xiamen,China
第一作者单位北师香港浸会大学
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Fan, Wentao,Huang, Hanyuan,Liang, Chenet al. Unsupervised meta-learning via spherical latent representations and dual VAE-GAN[J]. Applied Intelligence, 2023, 53(19): 22775-22788.
APA Fan, Wentao, Huang, Hanyuan, Liang, Chen, Liu, Xin, & Peng, Shu Juan. (2023). Unsupervised meta-learning via spherical latent representations and dual VAE-GAN. Applied Intelligence, 53(19), 22775-22788.
MLA Fan, Wentao,et al."Unsupervised meta-learning via spherical latent representations and dual VAE-GAN". Applied Intelligence 53.19(2023): 22775-22788.
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