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Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
[article]
2020
arXiv
pre-print
A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process. Recent breakthrough work by Khemakhem et al. (2019) on nonlinear ICA has answered this question for a broad class of conditional generative processes. We extend this important result in a direction relevant for application to real-world data. First, we generalize the theory to the case of unknown intrinsic problem
arXiv:2001.04872v1
fatcat:epijjewfgfeslbf66hlwuwyeny