Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Human Categorization of Natural Images Using Deep Feature
  Representations   Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visual domain in which it evolved and developed. We show that
representations derived from a convolutional neural network can be used to
model behavior over a database of >300,000 human natural image classifications,
and find that a group of models based on these representations perform well,
near the reliability of human judgments. Interestingly, this group includes
both exemplar and prototype models, contrasting with the dominance of exemplar
models in previous work. We are able to improve the performance of the
remaining models by preprocessing neural network representations to more
closely capture human similarity judgments.

37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph