Preprint Open access
Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces
Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These include signal-noise factorization (SNF), the ability to segregate signal from noise, and signal-signal factorization (SSF), the ability to segregate task-specific and task-ir …