Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Representational Learning for Offline Signature Verification using
  Multi-Loss Snapshot Ensemble of CNNs   Offline Signature Verification (OSV) is a challenging pattern recognition
task, especially in presence of skilled forgeries that are not available during
training. This study aims to tackle its challenges and meet the substantial
need for generalization for OSV by examining different loss functions for
Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking
two questions: 1. which classification loss provides more generalization for
feature learning in OSV? , and 2. How integration of different losses into a
unified multi-loss function lead to an improved learning framework? These
questions are studied based on analysis of three loss functions, including
cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to
complementary features of these losses, we combine them into a dynamic
multi-loss function and propose a novel ensemble framework for simultaneous use
of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of
several sequential trials. In each trial, a dominant loss function is selected
from the multi-loss set, and the remaining losses act as a regularizer.
Different trials learn diverse representations for each input based on
signature identification task. This multi-representation set is then employed
for the verification task. An ensemble of SVMs is trained on these
representations, and their decisions are finally combined according to the
selection of most generalizable SVM for each user. We conducted two sets of
experiments based on two different protocols of OSV, i.e., writer-dependent and
writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and
UT-SIG. Based on the writer-dependent OSV protocol, we achieved substantial
improvements over the best EERs in the literature. The results of the second
set of experiments also confirmed the robustness to the arrival of new users
enrolled in the OSV system.

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The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci