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Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language.
Maximum likelihood from incomplete data via the em algorithm
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Text classification from labeled and unlabeled documents using em
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Distance metric learning for large margin nearest neighbor classification
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Tri-training: exploiting unlabeled data using three classifiers
Z.-H. Zhou and M. Li · 2005
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Caltech-UCSD Birds 200
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Semi-supervised learning by disagreement
Z.-H. Zhou and M. Li · 2010
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Learning word embeddings efficiently with noise-contrastive estimation
A. Mnih and K. Kavukcuoglu · 2013
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From perception to conception: Learning multisensory representations
I. Yildirim · 2014
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Importance weighted autoencoders
Y. Burda, R. B. Grosse, and R. Salakhutdinov · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Unlabeled disentangling of gans with guided siamese networks
G. Yildirim, N. Jetchev, and U. Bergmann · 2018
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Deep multimodal representation learning: A survey
W. Guo, J. Wang, and S. Wang · 2019
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Learning deep representations by mutual information estimation and maximization
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio · 2019
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Sigan: Siamese generative adversarial network for identity-preserving face hallucination
C.-C. Hsu, C.-W. Lin, W.-T. Su, and G. Cheung · 2019
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Variational mixture-of-experts autoencoders for multi-modal deep generative models
Y. Shi, S. Narayanaswamy, B. Paige, and P. H. S. Torr · 2019
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Learning factorized multimodal representations
Y. H. Tsai, P. P. Liang, A. Zadeh, L. Morency, and R. Salakhutdinov · 2019
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