2016
Disentangling factors of variation in deep representation using adversarial training
NeurIPS 2016poster
We propose a deep generative model for learning to distill the hidden factors of variation within a set of labeled observations into two complementary codes. One code describes the factors of variation relevant to solving a specified task. The other code describes the remaining factors of variation…