Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks Rival the Representation of Primate IT Cortex for
  Core Visual Object Recognition   The primate visual system achieves remarkable visual object recognition
performance even in brief presentations and under changes to object exemplar,
geometric transformations, and background variation (a.k.a. core visual object
recognition). This remarkable performance is mediated by the representation
formed in inferior temporal (IT) cortex. In parallel, recent advances in
machine learning have led to ever higher performing models of object
recognition using artificial deep neural networks (DNNs). It remains unclear,
however, whether the representational performance of DNNs rivals that of the
brain. To accurately produce such a comparison, a major difficulty has been a
unifying metric that accounts for experimental limitations such as the amount
of noise, the number of neural recording sites, and the number trials, and
computational limitations such as the complexity of the decoding classifier and
the number of classifier training examples. In this work we perform a direct
comparison that corrects for these experimental limitations and computational
considerations. As part of our methodology, we propose an extension of "kernel
analysis" that measures the generalization accuracy as a function of
representational complexity. Our evaluations show that, unlike previous
bio-inspired models, the latest DNNs rival the representational performance of
IT cortex on this visual object recognition task. Furthermore, we show that
models that perform well on measures of representational performance also
perform well on measures of representational similarity to IT and on measures
of predicting individual IT multi-unit responses. Whether these DNNs rely on
computational mechanisms similar to the primate visual system is yet to be
determined, but, unlike all previous bio-inspired models, that possibility
cannot be ruled out merely on representational performance grounds.

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The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc