Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparative Analysis of Machine Learning Approaches for Automated Face
  Mask Detection During COVID-19   The World Health Organization (WHO) has recommended wearing face masks as one
of the most effective measures to prevent COVID-19 transmission. In many
countries, it is now mandatory to wear face masks, specially in public places.
Since manual monitoring of face masks is often infeasible in the middle of the
crowd, automatic detection can be beneficial. To facilitate that, we explored a
number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask
detection and evaluated them on two benchmark datasets. We also evaluated
transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this
context. We find that while the performances of all the models are quite good,
transfer learning models achieve the best performance. Transfer learning
improves the performance by 0.10\%--0.40\% with 30\% less training time. Our
experiment also shows these high-performing models are not quite robust for
real-world cases where the test dataset comes from a different distribution.
Without any fine-tuning, the performance of these models drops by 47\% in
cross-domain settings.

33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin