mlsanityData to 7 Oct 2026

Deep Variational Canonical Correlation Analysis

arXiv 1610.03454 ↗First posted 11 Oct 2016cs.LGcited by 44

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Abstract (arXiv)

We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks. We derive variational lower bounds of the data likelihood by parameterizing the posterior probability of the latent variables from the view that is available at test time. We also propose a variant of VCCA called VCCA-private that can, in addition to the "common variables" underlying both views, extract the "private variables" within each view, and disentangles the shared and private information for multi-view data without hard supervision. Experimental results on real-world datasets show that our methods are competitive across domains.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Oct 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 6; 2022: 10; 2023: 8; 2024: 8; 2025: 6; 2026: 5.

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  1. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 258
  2. 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 177
  3. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
  4. 2021Trusted Multi-View Classificationcited by 94
  5. 2023Identifiability Results for Multimodal Contrastive Learningcited by 58
  6. 2022A survey of multimodal deep generative modelscited by 47
  7. 2023Understanding Multimodal Contrastive Learning and Incorporating Unpaired Datacited by 43
  8. 2021Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspectivecited by 37
  9. 2021Deep Co-Attention Network for Multi-View Subspace Learningcited by 17
  10. 2022Gacs-Korner Common Information Variational Autoencodercited by 12
  11. 2021Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Expertscited by 9
  12. 2024A Survey on Deep Clustering: From the Prior Perspectivecited by 9
32 more
  1. 2021Disentangled Variational Information Bottleneck for Multiview Representation Learningcited by 8
  2. 2022Interpretability with full complexity by constraining feature informationcited by 7
  3. 2022Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audiocited by 6
  4. 2022Multi-View Independent Component Analysis with Shared and Individual Sourcescited by 6
  5. 2023Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Lossescited by 6
  6. 2023Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learningcited by 6
  7. 2023Revisiting Deep Generalized Canonical Correlation Analysiscited by 6
  8. 2021Discriminative Multimodal Learning via Conditional Priors in Generative Modelscited by 4
  9. 2023Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectivescited by 4
  10. 2025Disentanglement of Variations with Multimodal Generative Modelingcited by 4
  11. 2024Disentangling shared and private latent factors in multimodal Variational Autoencoderscited by 3
  12. 2024Identifiable Shared Component Analysis of Unpaired Multimodal Mixturescited by 3
  13. 2025Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoderscited by 3
  14. 2024Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensionscited by 2
  15. 2025Accurate Estimation of Mutual Information in High Dimensional Datacited by 2
  16. 2022Multimodal Generative Models for Bankruptcy Prediction Using Textual Datacited by 1
  17. 2023Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysiscited by 1
  18. 2024A tutorial on multi-view autoencoders using the multi-view-AE librarycited by 1
  19. 2024Unsupervised discovery of the shared and private geometry in multi-view datacited by 1
  20. 2024Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularizationcited by 1
  21. 2025Multi-view autoencoders for Fake News Detectioncited by 1
  22. 2026Content-Style Identification via Differential Independencecited by 1
  23. 2026Diff-CA: Separating Common and Salient Factors with Diffusion Modelscited by 1
  24. 2022Variational Interpretable Learning from Multi-view Datacited by 0
  25. 2022A Graphical Model for Fusing Diverse Microbiome Datacited by 0
  26. 2023Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methodscited by 0
  27. 2024Correlating Variational Autoencoders Natively For Multi-View Imputationcited by 0
  28. 2025InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysiscited by 0
  29. 2025No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labelscited by 0
  30. 2026H"older++: Improving the Quality-Coherence Trade-off in Multimodal VAEscited by 0
  31. 2026TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Treescited by 0
  32. 2026Structured Latent Modeling for Supervised Multimodal Information Decompositioncited by 0