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Unsupervised meta-learning via spherical latent representations and dual VAE-GAN

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Abstract

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.

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Data Availability

The data sets analysed during the current study are available at: CIFAR-FS : https://www.cs.toronto.edu/~kriz/cifar.html, Mini-ImageNet: https://drive.google.com/file/d/0B3Irx3uQNoBMQ1FlNXJsZUdYWEE/view, FC-100: https://github.com/ServiceNow/TADAM/tree/master/datasets and Omniglot: https://github.com/brendenlake/omniglot/tree/master/python.

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Acknowledgements

The completion of this work was supported in part by the National Natural Science Foundation of China (62276106), the UIC Start-up Research Fund (UICR0700056-23), the Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science (2022B1212010006), the Guangdong Higher Education Upgrading Plan (2021-2025) of “Rushing to the Top, Making Up Shortcomings and Strengthening Special Features” (R0400001-22), and the Artificial Intelligence and Data Science Research Hub (AIRH) of BNU-HKBU United International College (UIC).

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Authors and Affiliations

  1. Department of Computer Science, Beijing Normal University-Hong Kong Baptist University United International College (BNU-HKBU UIC), Zhuhai, Guangdong, China

    Wentao Fan

  2. Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China

    Wentao Fan

  3. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Hanyuan Huang, Chen Liang, Xin Liu & Shu-Juan Peng

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  1. Wentao Fan
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Correspondence to Wentao Fan.

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Fan, W., Huang, H., Liang, C. et al. Unsupervised meta-learning via spherical latent representations and dual VAE-GAN. Appl Intell 53, 22775–22788 (2023). https://doi.org/10.1007/s10489-023-04760-9

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