Adjoint Inversion Reveals Holographic Superposition and Destructive Interference in CNN Classifiers
The research developed an adjoint inversion framework to understand how CNN channels combine scene information for classification. It found that deep features exhibit 'holographic superposition,' where every channel retains global scene content, and classification occurs through 'destructive interference.' This means classifiers cancel out background noise while focusing only on foreground information.