Dual-Manifold Geometry Guided Representation Learning: Adaptive Coupling between Kernel and Data Spaces
Deep representation learning has primarily focused on how features evolve across network layers, while largely overlooking the structured geometry embedded in network parameters. We introduce a dual-manifold perspective in which each convolutional layer contains two coupled geometric spaces: a Kernel Manifold induced b...