"Correlational neural networks" - looking at learning from multiple perspectives of the same thing to increase representation learning.

@article{chandar2016neuralcompjour, author = {Chandar, Sarath and Khapra, Mitesh M and Larochelle, Hugo and Ravindran, Balaraman}, date-added = {2024-08-01 10:47:30 -0400}, date-modified = {2024-08-01 10:50:01 -0400}, journal = {Neural Computation}, keywords = {correlation-learning, machine-learning, inductive-bias, autoencoders}, number = {2}, pages = {257--285}, pdf = {https://www.researchgate.net/profile/Balaraman-Ravindran/publication/275588055_Correlational_Neural_Networks/links/55ed84d308ae21d099c75c00/Correlational-Neural-Networks.pdf}, publisher = {MIT Press}, title = {Correlational neural networks}, venue-short = {NeuralCompJour}, volume = {28}, year = {2016}}

Chapter 21 "Adversarial Autonencoders" from our book "Elements of Dimensionality Reduction and Manifold Learning", Springer 2023.

https://udlguidelines.cast.org/