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Research Review Notes

Summaries of academic research papers

JADE: Joint Autoencoders for Dis-Entanglement


Idea

The authors motivate the idea of using an auxilliary dataset that has abundant labeled samples to supplement a dataset that has only a few labeled samples, as long as the two datasets have at least one factor of variation in common. This is done by distentangling the content representation from the style using a variational autoencoder.

Method

Observations