Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deleting object selective units in a fully-connected layer of deep
  convolutional networks improves classification performance   Neurons in the primate visual cortices show a wide range of stimulus
selectivity. Some neurons respond to only a small fraction of stimulus images,
whereas others respond to many stimulus images in a non-selective manner. It is
unclear how stimulus selective and non-selective neurons contribute to visual
object recognition. Herein, we examined the relationship between stimulus
selectivity and the effect of deletion of units on task performance using fully
a connected layer of two types of deep convolutional neural networks (DCNNs).
Deleting a stimulus selective unit caused slight improvements of task
performance, whereas deleting stimulus non-selective units caused a significant
decrease in task performance. However, these findings do not imply that
stimulus selective units have no use for the task. Indeed, better performance
was obtained when the networks consisted of both stimulus selective and
non-selective units.

17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph