Customization of latent space in semi-supervised Variational AutoEncoder

Author:

An SeunghwanORCID,Jeon Jong-JuneORCID

Funder

University of Seoul

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference26 articles.

1. D.P. Kingma, M. Welling, Auto-encoding variational bayes, CoRR abs/1312.6114.

2. Semi-supervised learning with deep generative models;Kingma,2014

3. Mixture variational autoencoders;Jiang;Pattern Recognit. Lett.,2019

4. H.-Z. Feng, K. Kong, M. Chen, T. Zhang, M. Zhu, W. Chen, Shot-vae: Semi-supervised deep generative models with label-aware elbo approximations, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 7413–7421.

5. S. Hajimiri, A. Lotfi, M.S. Baghshah, Semi-supervised disentanglement of class-related and class-independent factors in VAE, arXiv preprint arXiv:2102.00892.

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