Influence of CNN Layer Depth on the Perception of Spiral Illusions

抄録

This study investigated how the depth of Convolutional Neural Networks (CNNs) affects the perception of spiral illusions. Three CNNs with different layer depths were trained to distinguish between spirals and concentric circles and were evaluated using 14 illusion images. The results showed that shallower CNNs were more likely to classify the images as a spiral than deeper ones. These findings suggest that CNN depth influences the perception of visual illusions, providing insights into both artificial visual systems and human visual cognition.