Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Light weight and Hybrid Deep Learning Model based Online Signature
  Verification   The augmented usage of deep learning-based models for various AI related
problems are as a result of modern architectures of deeper length and the
availability of voluminous interpreted datasets. The models based on these
architectures require huge training and storage cost, which makes them
inefficient to use in critical applications like online signature verification
(OSV) and to deploy in resource constraint devices. As a solution, in this
work, our contribution is two-fold. 1) An efficient dimensionality reduction
technique, to reduce the number of features to be considered and 2) a
state-of-the-art model CNN-LSTM based hybrid architecture for online signature
verification. Thorough experiments on the publicly available datasets MCYT,
SUSIG, SVC confirms that the proposed model achieves better accuracy even with
as low as one training sample. The proposed models yield state-of-the-art
performance in various categories of all the three datasets.

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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci