Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a Robust WiFi-based Fall Detection with Adversarial Data
  Augmentation   Recent WiFi-based fall detection systems have drawn much attention due to
their advantages over other sensory systems. Various implementations have
achieved impressive progress in performance, thanks to machine learning and
deep learning techniques. However, many of such high accuracy systems have low
reliability as they fail to achieve robustness in unseen environments. To
address that, this paper investigates a method of generalization through
adversarial data augmentation. Our results show a slight improvement in deep
learning-systems in unseen domains, though the performance is not significant.

16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: econ
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph