Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
This work addresses representation learning for real-world data by providing a theoretical and practical extension to nonlinear ICA, though it is incremental in nature.
The paper tackles the problem of reconstructing true latent variables from complex generative processes by extending nonlinear ICA theory to unknown dimensions and introducing General Incompressible-flow Networks (GIN), achieving exact recovery of 22 informative latent variables from EMNIST data.
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 dimension and prove that in some special (but not very restrictive) cases, informative latent variables will be automatically separated from noise by an estimating model. Furthermore, the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process, up to a trivial component-wise transformation. Second, we introduce a modification of the RealNVP invertible neural network architecture (Dinh et al. (2016)) which is particularly suitable for this type of problem: the General Incompressible-flow Network (GIN). Experiments on artificial data and EMNIST demonstrate that theoretical predictions are indeed verified in practice. In particular, we provide a detailed set of exactly 22 informative latent variables extracted from EMNIST.