SpringerLink
Log in

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

  • Published:
Applied Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

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.

References

  1. Antoniou A, Storkey A (2019) Assume, augment and learn: Unsupervised few-shot meta-learning via random labels and data augmentation. ar**v:1902.09884

  2. Aytekin C, Ni X, Cricri F, Aksu E (2018) Clustering and unsupervised anomaly detection with l2 normalized deep auto-encoder representations. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp 1–6

  3. Banerjee A, Dhillon I, Ghosh J, Sra S (2005) Clustering on the unit hypersphere using von Mises-Fisher distributions. Journal of Machine Learning Research 6:1345–1382

    MathSciNet  MATH  Google Scholar 

  4. Berthelot D, Raffel C, Roy A, Goodfellow I (2019) Understanding and improving interpolation in autoencoders via an adversarial regularizer. In: International Conference on Learning Representations

  5. Bertinetto L, Henriques JF, Torr P, Vedaldi A (2019) Meta-learning with differentiable closed-form solvers. In: International Conference on Learning Representations

  6. Caron M, Bojanowski P, Joulin A, Douze M (2018) Deep clustering for unsupervised learning of visual features. In: Proceedings of the European conference on computer vision (ECCV), pp 132–149

  7. Davidson TR, Falorsi L, Cao ND, Kipf T, Tomczak JM (2018a) Hyperspherical variational auto-encoders. In: Proceedings of the Conference on uncertainty in artificial intelligence, pp 856–865

  8. Davidson TR, Falorsi L, De Cao N, Kipf T, Tomczak JM (2018b) Hyperspherical variational auto-encoders. In: 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, Association For Uncertainty in Artificial Intelligence (AUAI), pp 856–865

  9. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, Ieee, pp 248–255

  10. Fan W, Bouguila N (2020) Spherical data clustering and feature selection through nonparametric Bayesian mixture models with von mises distributions. Engineering Applications of Artificial Intelligence 94(103):781

    Google Scholar 

  11. Fan W, Hou W (2022) Unsupervised modeling and feature selection of sequential spherical data through nonparametric hidden markov models. International Journal of Machine Learning and Cybernetics 13(10):3019–3029

    Article  Google Scholar 

  12. Fan W, Bouguila N, Du JX, Liu X (2019) Axially symmetric data clustering through dirichlet process mixture models of watson distributions. IEEE Transactions on Neural Networks and Learning Systems 30(6):1683–1694

    Article  MathSciNet  Google Scholar 

  13. Fan W, Yang L, Bouguila N, Chen Y (2020) Sequentially spherical data modeling with hidden markov models and its application to fmri data analysis. Knowledge-Based Systems 206(106):341

    Google Scholar 

  14. Fan W, Yang L, Bouguila N (2022) Unsupervised grouped axial data modeling via hierarchical bayesian nonparametric models with watson distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12):9654–9668

    Article  Google Scholar 

  15. Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, PMLR, pp 1126–1135

  16. Finn C, Xu K, Levine S (2018) Probabilistic model-agnostic meta-learning. Advances in neural information processing systems 31

  17. Flennerhag S, Rusu A, Pascanu R, Visin F, Yin H, Hadsell R (2020) Meta-learning with warped gradient descent. In: International Conference on Learning Representations 2020

  18. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in neural information processing systems 27

  19. Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: More features from cheap operations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1580–1589

  20. Hewitt LB, Nye MI, Gane A, Jaakkola TS, Tenenbaum JB (2018) The variational homoencoder: Learning to learn high capacity generative models from few examples. In: Conference on Uncertainty in Artificial Intelligence, Association For Uncertainty in Artificial Intelligence (AUAI)

  21. Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33:6840–6851

    Google Scholar 

  22. Hospedales T, Antoniou A, Micaelli P, Storkey A (2021) Meta-learning in neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence 44(9):5149–5169

    Google Scholar 

  23. Hsu K, Levine S, Finn C (2019) Unsupervised learning via meta-learning. In: International Conference on Learning Representations

  24. Jiang Z, Zheng Y, Tan H, Tang B, Zhou H (2017) Variational deep embedding: an unsupervised and generative approach to clustering. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp 1965–1972

  25. Keskar NS, Nocedal J, Tang PTP, Mudigere D, Smelyanskiy M (2017) On large-batch training for deep learning: Generalization gap and sharp minima. In: 5th International Conference on Learning Representations, ICLR 2017

  26. Khodadadeh S, Boloni L, Shah M (2019) Unsupervised meta-learning for few-shot image classification. Advances in neural information processing systems 32

  27. Khodadadeh S, Zehtabian S, Vahidian S, Wang W, Lin B, Boloni L (2021) Unsupervised meta-learning through latent-space interpolation in generative models. In: International Conference on Learning Representations

  28. Kingma D, Ba J (2014) Adam: A method for stochastic optimization. Computer Science

  29. Kingma DP, Welling M (2013) Auto-encoding variational bayes. In: International Conference on Learning Representations

  30. Lee DB, Min D, Lee S, Hwang SJ (2020) Meta-gmvae: Mixture of gaussian vae for unsupervised meta-learning. In: International Conference on Learning Representations

  31. Ley C, Verdebout T (2018) Applied Directional Statistics: Modern Methods and Case Studies. Chapman and Hall/CRC

  32. Li Z, Liu H, Zhang Z, Liu T, **ong NN (2022) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Transactions on Neural Networks and Learning Systems 33(8):3961–3973

    Article  MathSciNet  Google Scholar 

  33. Liu H, Nie H, Zhang Z, Li YF (2021) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310–322

    Article  Google Scholar 

  34. Liu H, Fang S, Zhang Z, Li D, Lin K, Wang J (2022) MFDNet: Collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Transactions on Multimedia 24:2449–2460

    Article  Google Scholar 

  35. Liu H, Liu T, Chen Y, Zhang Z, Li YF (2022b) Ehpe: Skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Transactions on Multimedia pp 1–12, 10.1109/TMM.2022.3197364

  36. Liu H, Liu T, Zhang Z, Sangaiah AK, Yang B, Li Y (2022) ARHPE: Asymmetric relation-aware representation learning for head pose estimation in industrial human-computer interaction. IEEE Transactions on Industrial Informatics 18(10):7107–7117

    Article  Google Scholar 

  37. Liu H, Zheng C, Li D, Shen X, Lin K, Wang J, Zhang Z, Zhang Z, **ong NN (2022) EDMF: Efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Transactions on Industrial Informatics 18(7):4361–4371

    Article  Google Scholar 

  38. Liu T, Liu H, Li YF, Chen Z, Zhang Z, Liu S (2020) Flexible ftir spectral imaging enhancement for industrial robot infrared vision sensing. IEEE Transactions on Industrial Informatics 16(1):544–554

    Article  Google Scholar 

  39. Liu T, Wang J, Yang B, Wang X (2021) NGDNet: Nonuniform gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436:210–220

    Article  Google Scholar 

  40. Liu X, Hu Z, Ling H, Cheung YM (2021) Mtfh: A matrix tri-factorization hashing framework for efficient cross-modal retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(3):964–981

    Article  Google Scholar 

  41. Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. ar**v preprint http://arxiv.org/abs/1803.02999ar**v:1803.02999

  42. Oreshkin B, Rodríguez López P, Lacoste A (2018) Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in neural information processing systems 31

  43. Qin T, Li W, Shi Y, Gao Y (2020) Diversity helps: Unsupervised few-shot learning via distribution shift-based data augmentation. http://arxiv.org/abs/2004.05805ar**v:2004.05805

  44. Rusu AA, Rao D, Sygnowski J, Vinyals O, Pascanu R, Osindero S, Hadsell R (2019) Meta-learning with latent embedding optimization. In: International Conference on Learning Representations

  45. Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Advances in neural information processing systems 30

  46. Sung F, Yang Y, Zhang L, **ang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1199–1208

  47. Taghia J, Ma Z, Leijon A (2014) Bayesian estimation of the von mises-fisher mixture model with variational inference. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(9):1701–1715

    Article  Google Scholar 

  48. Thrun S, Pratt L (2012) Learning to learn. Springer Science & Business Media

  49. Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al (2016) Matching networks for one shot learning. Advances in neural information processing systems 29

  50. Wu M, Choi K, Goodman N, Ermon S (2020) Meta-amortized variational inference and learning. Proceedings of the AAAI Conference on Artificial Intelligence 34:6404–6412

    Article  Google Scholar 

  51. Xu H, Wang J, Li H, Ouyang D, Shao J (2021) Unsupervised meta-learning for few-shot learning. Pattern Recognition 116(107):951

    Google Scholar 

  52. Xu J, Durrett G (2018) Spherical latent spaces for stable variational autoencoders. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 4503–4513

  53. Yan M, Chen Y, Chen Y, Zeng G, Hu X, Du J (2022) A lightweight weakly supervised learning segmentation algorithm for imbalanced image based on rotation density peaks. Knowledge-Based Systems 244(108):513

    Google Scholar 

  54. Yang L, Fan W, Bouguila N (2022) Clustering analysis via deep generative models with mixture models. IEEE Transactions on Neural Networks and Learning Systems 33(1):340–350

    Article  MathSciNet  Google Scholar 

  55. Yang X, Deng C, Zheng F, Yan J, Liu W (2019) Deep spectral clustering using dual autoencoder network pp 4066–4075

  56. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848–6856

Download references

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).

Author information

Authors and Affiliations

  1. Department of Computer Science, Bei**g 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, **amen, China

    Hanyuan Huang, Chen Liang, **n Liu & Shu-Juan Peng

Authors
  1. Wentao Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanyuan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. **n Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shu-Juan Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wentao Fan.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest to this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-04760-9

Keywords

Search

Navigation