ResNet-50

Internal Oppenheim-Lim test reveals phase/sign identity codes shared across image classifier architectures

A new arXiv preprint applies a causal intervention inspired by Oppenheim and Lim (1981) to probe whether trained image classifiers encode identity in Fourier phase rather than magnitude within their hidden layers. By transplanting phase or sign components between images at chosen layers in PRISM2D, GFNet, ViT-B/16, and ResNet-50, the authors find that predictions follow the phase/sign donor across all tested architectures, with image-specific magnitude largely dispensable. ResNet-50 requires a pre-ReLU intervention to reveal a latent sign code, exposing how rectification and readout geometry shape the basis in which the code is expressed. The findings offer a mechanistic account of the texture–shape gap between CNNs and attention-based models.