<ul><li>Generative networks have shown success in learning complex data distributions, but their theoretical foundation is unclear.</li><li>Previous theory suggested that the latent dimension needs to be at least equal to the intrinsic dimension of the data manifold to approximate its distribution.</li><li>However, a new study challenges this requirement by demonstrating that generative networks can approximate distributions on lower-dimensional manifolds from inputs of any dimension.</li><li>This finding implies a trade-off between approximation error, dimensionality, and model complexity in generative networks.</li></ul>

Deep Generative Models: Complexity, Dimensionality, and Approximation

Discover more