Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference   Modern convolutional neural networks apply the same operations on every pixel
in an image. However, not all image regions are equally important. To address
this inefficiency, we propose a method to dynamically apply convolutions
conditioned on the input image. We introduce a residual block where a small
gating branch learns which spatial positions should be evaluated. These
discrete gating decisions are trained end-to-end using the Gumbel-Softmax
trick, in combination with a sparsity criterion. Our experiments on CIFAR,
ImageNet and MPII show that our method has better focus on the region of
interest and better accuracy than existing methods, at a lower computational
complexity. Moreover, we provide an efficient CUDA implementation of our
dynamic convolutions using a gather-scatter approach, achieving a significant
improvement in inference speed with MobileNetV2 residual blocks. On human pose
estimation, a task that is inherently spatially sparse, the processing speed is
increased by 60% with no loss in accuracy.

8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat