Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs   To accelerate deep CNN models, this paper proposes a novel spatially adaptive
framework that can dynamically generate pixel-wise sparsity according to the
input image. The sparse scheme is pixel-wise refined, regional adaptive under a
unified importance map, which makes it friendly to hardware implementation. A
sparse controlling method is further presented to enable online adjustment for
applications with different precision/latency requirements. The sparse model is
applicable to a wide range of vision tasks. Experimental results show that this
method efficiently improve the computing efficiency for both image
classification using ResNet-18 and super resolution using SRResNet. On image
classification task, our method can save 30%-70% MACs with a slightly drop in
top-1 and top-5 accuracy. On super resolution task, our method can reduce more
than 90% MACs while only causing around 0.1 dB and 0.01 decreasing in PSNR and
SSIM. Hardware validation is also included.

2
The categories of this paper are: math
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an