Models implemented

Below is a table with the models contained within this repository and links to the original papers.

[1] Antelmi, Luigi & Ayache, Nicholas & Robert, Philippe & Lorenzi, Marco. (2019). Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data.

[2] Sohn, K., Lee, H., & Yan, X. (2015). Learning Structured Output Representation using Deep Conditional Generative Models. NIPS.

[3] Wang, Weiran & Lee, Honglak & Livescu, Karen. (2016). Deep Variational Canonical Correlation Analysis.

[4] Yuge Shi, N. Siddharth, Brooks Paige, and Philip H. S. Torr. 2019. Variational mixture-of-experts autoencoders for multi-modal deep generative models. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 1408, 15718–15729.

[5] Wu, Mike & Goodman, Noah. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning.

[6] Suzuki, Masahiro and Nakayama, Kotaro and Matsuo, Yutaka. (2016). Joint Multimodal Learning with Deep Generative Models.

[7] Sutter, Thomas & Daunhawer, Imant & Vogt, Julia. (2021). Generalized Multimodal ELBO.

[8] Hwang, HyeongJoo and Kim, Geon-Hyeong and Hong, Seunghoon and Kim, Kee-Eung. Multi-View Representation Learning via Total Correlation Objective. 2021. NeurIPS

[9] Sutter, Thomas & Daunhawer, Imant & Vogt, Julia. (2021). Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. Advances in Neural Information Processing Systems. 33.

[10] Lawry Aguila, A., Chapman, J., Altmann, A. (2023). Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities. arXiv

[11] Cao, Y., & Fleet, D. (2014). Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions. arXiv.

[12] Zbontar, J., Jing, L., Misra, I., LeCun, Y., & Deny, S. (2021). Barlow Twins: Self-Supervised Learning via Redundancy Reduction. International Conference on Machine Learning.

[13] Chapman et al., (2021). CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework. Journal of Open Source Software, 6(68), 3823, https://doi.org/10.21105/joss.03823

[14] Lee, M., Pavlovic, V. (2020). Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations. arXiv.