Discovering Nonlinear Symmetries in High-Dimensional Data Using Latent Space Representations

LaLiGAN, a novel generative modeling framework, can discover nonlinear symmetries in high-dimensional data by decomposing the group action into nonlinear mappings between the data space and a latent space, and a linear group representation in the latent space.