Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Localizing Interpretable Multi-scale informative Patches Derived from
  Media Classification Task   Deep convolutional neural networks (CNN) always depend on wider receptive
field (RF) and more complex non-linearity to achieve state-of-the-art
performance, while suffering the increased difficult to interpret how relevant
patches contribute the final prediction. In this paper, we construct an
interpretable AnchorNet equipped with our carefully designed RFs and linearly
spatial aggregation to provide patch-wise interpretability of the input media
meanwhile localizing multi-scale informative patches only supervised on
media-level labels without any extra bounding box annotations. Visualization of
localized informative image and text patches show the superior multi-scale
localization capability of AnchorNet. We further use localized patches for
downstream classification tasks across widely applied networks. Experimental
results demonstrate that replacing the original inputs with their patches for
classification can get a clear inference acceleration with only tiny
performance degradation, which proves that localized patches can indeed retain
the most semantics and evidences of the original inputs.

5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th