Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps   This paper addresses the visualisation of image classification models, learnt
using deep Convolutional Networks (ConvNets). We consider two visualisation
techniques, based on computing the gradient of the class score with respect to
the input image. The first one generates an image, which maximises the class
score [Erhan et al., 2009], thus visualising the notion of the class, captured
by a ConvNet. The second technique computes a class saliency map, specific to a
given image and class. We show that such maps can be employed for weakly
supervised object segmentation using classification ConvNets. Finally, we
establish the connection between the gradient-based ConvNet visualisation
methods and deconvolutional networks [Zeiler et al., 2013].

7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin