Image classifiers internally separate identity (encoded in phase/sign) from magnitude information, similar to how human vision works, but different architectures expose this separation through different mechanisms—a mechanistic insight that could improve model design and interpretability.
This paper investigates whether trained image classifiers internally rely on phase information (like natural images do) by swapping phase and magnitude between images at different network layers.