Understanding the Local Geometry of Generative Model Manifolds
Understanding the Local Geometry of Generative Model Manifolds
Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pre-trained generative model, the common way to evaluate the quality of the manifold representation learned, is by computing global metrics like Fr\'echet Inception Distance using a large number of generated …