Brain-Cog-Lab/AbuttingGratingIllusion
The code for "Challenging Deep Learning Models with Image Distortion based on the Abutting Grating Illusion"
This project helps evaluate how robust your deep learning models are at recognizing objects when images are subtly distorted by visual illusions, specifically the 'abutting grating illusion'. It takes existing image datasets and applies this unique distortion, then measures how well various deep learning models perform compared to human perception. Researchers in AI safety, cognitive science, or model interpretability would use this to understand model limitations.
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Use this if you need to test the robustness of your deep learning vision models against human-like perceptual distortions, rather than just mathematical noise or transformations.
Not ideal if you're looking for general image augmentation techniques or solutions for standard image classification problems without a focus on cognitive robustness.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 01, 2023
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