The Importance of Phase in Neural Representations: An Internal Oppenheim-Lim Test of Image Classifiers
arXiv:2606.17037v1 Announce Type: cross Abstract: Oppenheim and Lim (1981) showed that natural images stay recognizable when reconstructed from their Fourier phase alone, while the magnitude carries little of their identity. We ask whether trained image classifiers reproduce this asymmetry inside their hidden layers, and we test it causally: given two images, we transplant the phase of one onto the magnitude of the other at a chosen layer and record which image the prediction follows. In PRISM2D, GFNet, and ViT-B/16 the prediction follows the phase or sign donor, and deleting all image-specifi
This research builds on contemporary understanding of neural networks and their representational capabilities, leveraging recent advancements in AI interpretability and model analysis.
Understanding how AI classifies images, particularly the dominant role of phase information, is crucial for developing robust, explainable, and potentially more efficient AI systems.
This research suggests a potential paradigm shift in how AI models for vision are designed and understood, emphasizing phase over magnitude, which could lead to novel architectures and training methodologies.
- · AI interpretability researchers
- · Computer vision developers
- · AI hardware architects
- · AI models reliant solely on magnitude
- · Developers ignoring representational biases
This research refines our understanding of how image classifiers process visual information at a fundamental level.
Future computer vision models might be explicitly designed to prioritize or optimize for phase information, leading to more robust and efficient systems.
These insights could inform the development of biomimetic AI, drawing parallels between artificial and biological visual processing, or lead to new forms of adversarial attacks or defenses.
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Read at arXiv cs.AI