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Found 3 papers, 1 papers with code
Synthesizing Visual Illusions Using Generative Adversarial Networks
Visual illusions are a very useful tool for vision scientists, because they allow them to better probe the limits, thresholds and errors of the visual system.
Convolutional Neural Networks Can Be Deceived by Visual Illusions
In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size.
Convolutional Neural Networks Deceived by Visual Illusions
In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size.
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