Denoising Models Develop Human-Like Perceptual Illusion Representations Across Architectures
Deep neural networks trained on natural images are shown to produce outputs consistent with human observers for brightness illusions. While this phenomenon has been documented across architectures, all evidence, to date, is measured at the output level: restored pixels, decoded trajectories, or classification decisions. Whether these models actually represent illusions internally, and if so where and how, remains unknown. We show that denoising models develop illusion-sensitive representations at specific internal layers, across varied architectures. Specifically, we identify the layers and channels that discriminate illusory from physically matched control regions. We show that the denoi...
| Authors | Gautam Ranka, Paras Chopra |
|---|---|
| Primary category | cs.CV |
| Published | 19 July 2026 |
| Last updated at source | 8 September 2026 |
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