Deep Convolutional Networks do not Perceive Illusory Contours
- Nicholas Baker, Psychology, University of California, Los Angeles, Los Angeles, California, United States
- Gennady Erlikhman, Psychology, University of Nevada, Reno, Reno, Nevada, United States
- Philip J Kellman, Psychology, University of California, Los Angeles, Los Angeles, California, United States
- Hongjing Lu, UCLA, Los Angeles, California, United States
AbstractDeep learning networks have shown impressive performance in object recognition. We used the classification image method to probe whether a deep learning model employs the same features as humans in perceiving real and illusory contours. We adopted a deep learning network, pre-trained with natural images, and retrained the decision layer with laboratory stimuli to perform shape discrimination in the “fat/thin” task. We tested the network with real and illusory contour stimuli contaminated with luminance noise. We found that deep networks trained on natural images can be readily adapted to discriminate between psychophysical stimuli with an extremely high degree of accuracy. However, deep learning networks do not appear to represent illusory contours where they may aid performance in the fat/thin task, a process automatically performed in human vision. This divergence indicates an important difference between the kinds of visual representations formed by deep networks and by humans.
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