RT Journal Article
A1 Peterson, Joshua
A1 Aghi, Krishan
A1 Suchow, Jordan
A1 Ku, Alexander
A1 Griffiths, Thomas
T1 Sampling from object and scene representations using deep feature spaces
JF Journal of Vision
JO Journal of Vision
YR 2018
DO 10.1167/18.10.403
VO 18
IS 10
SP 403
OP 403
SN 1534-7362
AB  Decades of research on how people represent visual categories have yielded a variety of formal theories, but validating them with naturalistic visual stimuli such as objects and scenes remains a challenge. The key problems are that human visual category representations cannot be directly observed and designing informative experiments using rich visual stimuli such as photographs requires having a reasonable representation of these images. Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images. Here we outline a method for estimating the structure of human visual categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners. We provide qualitative and quantitative results as a proof of concept for the feasibility of the method. Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories. Meeting abstract presented at VSS 2018 
RD 3/8/2021
UL https://doi.org/10.1167/18.10.403