Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face
  Detection   Deploying deep learning based face detectors on edge devices is a challenging
task due to the limited computation resources. Even though binarizing the
weights of a very tiny network gives impressive compactness on model size (e.g.
240.9 KB for IFQ-Tinier-YOLO), it is not tiny enough to fit in the embedded
devices with strict memory constraints. In this paper, we propose DupNet which
consists of two parts. Firstly, we employ weights with duplicated channels for
the weight-intensive layers to reduce the model size. Secondly, for the
quantization-sensitive layers whose quantization causes notable accuracy drop,
we duplicate its input feature maps. It allows us to use more weights channels
for convolving more representative outputs. Based on that, we propose a very
tiny face detector, DupNet-Tinier-YOLO, which is 6.5X times smaller on model
size and 42.0% less complex on computation and meanwhile achieves 2.4% higher
detection than IFQ-Tinier-YOLO. Comparing with the full precision Tiny-YOLO,
our DupNet-Tinier-YOLO gives 1,694.2X and 389.9X times savings on model size
and computation complexity respectively with only 4.0% drop on detection rate
(0.880 vs. 0.920). Moreover, our DupNet-Tinier-YOLO is only 36.9 KB, which is
the tiniest deep face detector to our best knowledge.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex