Artificial Intelligence #neural representations#phase
Phase, Not Magnitude, Drives Image Classifier Predictions, New Research Reveals
A new study by Yıldırım tests whether image classifiers reproduce the Oppenheim-Lim phase dominance inside their hidden layers. By transplanting phase from one image to magnitude of another, the research finds that in architectures like ViT-B/16 and GFNet, predictions follow the phase donor, and removing image-specific magnitude barely affects accuracy. ResNet-50 exhibits a latent sign code before ReLU activation.
Jun 16, 2026 1 source