Adapting Deep Network Features to Capture Psychological Representations

Understand

Deep neural networks have become increasingly successful at solving classic perception problems such as object recognition, semantic segmentation, and scene understanding, often reaching or surpassing human-level accuracy.

• This success is due in part to the ability of DNNs to learn useful representations of high-dimensional inputs, a problem that humans must also solve.
• We examine the relationship between the representations learned by these networks and human psychological representations recovered from similarity judgments.
• We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images.