Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What Do Deep CNNs Learn About Objects?   Deep convolutional neural networks learn extremely powerful image
representations, yet most of that power is hidden in the millions of deep-layer
parameters. What exactly do these parameters represent? Recent work has started
to analyse CNN representations, finding that, e.g., they are invariant to some
2D transformations Fischer et al. (2014), but are confused by particular types
of image noise Nguyen et al. (2014). In this work, we delve deeper and ask: how
invariant are CNNs to object-class variations caused by 3D shape, pose, and
photorealism?

2
The categories of this paper are: math
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ