Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold.
Anupam ChaudhuriAnj SimmonsMohamed AbdelrazekPublished in: CoRR (2024)
Keyphrases
- manifold learning
- low dimensional
- euclidean space
- appearance manifolds
- manifold structure
- riemannian manifolds
- tangent space
- manifold alignment
- low dimensional manifolds
- manifold embedding
- principal curves
- laplacian eigenmaps
- locally linear
- high dimensional
- information geometry
- arbitrary dimension
- nonlinear manifold
- geometric structure
- lie group
- feature space
- nonlinear dimensionality reduction
- dimension reduction
- latent space
- cell complexes
- high dimensional data
- higher dimensional
- configuration space
- manifold learning algorithm
- smooth manifold
- appearance model
- human faces
- synthetic data
- diffusion maps
- unsupervised learning
- generative model
- facial expressions
- semi supervised
- multi agent