Title: Evaluating (and improving) the correspondence between deep neural networks and human representations,
Abstract: Decades of psychological research have been aimed at modeling how people
learn features and categories. The empirical validation of these theories is
often based on artificial stimuli with simple representations. Recently, deep
neural networks have reached or surpassed human accuracy on tasks such as
identifying objects in natural images. These networks learn representations of
real-world stimuli that can potentially be leveraged to capture psychological
representations. We find that state-of-the-art object classification networks
provide surprisingly accurate predictions of human similarity judgments for
natural images, but fail to capture some of the structure represented by
people. We show that a simple transformation that corrects these discrepancies
can be obtained through convex optimization. We use the resulting
representations to predict the difficulty of learning novel categories of
natural images. Our results extend the scope of psychological experiments and
computational modeling by enabling tractable use of large natural stimulus
sets.