Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distilling Critical Paths in Convolutional Neural Networks   Neural network compression and acceleration are widely demanded currently due
to the resource constraints on most deployment targets. In this paper, through
analyzing the filter activation, gradients, and visualizing the filters'
functionality in convolutional neural networks, we show that the filters in
higher layers learn extremely task-specific features, which are exclusive for
only a small subset of the overall tasks, or even a single class. Based on such
findings, we reveal the critical paths of information flow for different
classes. And by their intrinsic property of exclusiveness, we propose a
critical path distillation method, which can effectively customize the
convolutional neural networks to small ones with much smaller model size and
less computation.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int