Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Teaching Compositionality to CNNs   Convolutional neural networks (CNNs) have shown great success in computer
vision, approaching human-level performance when trained for specific tasks via
application-specific loss functions. In this paper, we propose a method for
augmenting and training CNNs so that their learned features are compositional.
It encourages networks to form representations that disentangle objects from
their surroundings and from each other, thereby promoting better
generalization. Our method is agnostic to the specific details of the
underlying CNN to which it is applied and can in principle be used with any
CNN. As we show in our experiments, the learned representations lead to feature
activations that are more localized and improve performance over
non-compositional baselines in object recognition tasks.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess