Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with
  Iterative Self-Training   Sleep staging is of great importance in the diagnosis and treatment of sleep
disorders. Recently, numerous data driven deep learning models have been
proposed for automatic sleep staging. They mainly train the model on a large
public cohort labeled sleep dataset and test it on a smaller one with subjects
of interest. However, they usually assume that the train and test data are
drawn from the same distribution, which may not hold in real-world scenarios.
Unsupervised domain adaption (UDA) has been recently developed to handle this
domain shift problem. However, previous UDA methods applied for sleep staging
has two main limitations. First, they rely on a totally shared model for the
domain alignment, which may lose the domain-specific information during feature
extraction. Second, they only align the source and target distributions
globally without considering the class information in the target domain, which
hinders the classification performance of the model while testing. In this
work, we propose a novel adversarial learning framework called ADAST to tackle
the domain shift problem in the unlabeled target domain. First, we develop
unshared attention mechanism to preserve the domain-specific features in the
source and target domains. Second, we design an iterative self-training
strategy to align the fine-grained class distributions for the source and
target domains via target domain pseudo labels. We also propose dual distinct
classifiers to increase the robustness and quality of the pseudo labels. The
experimental results on six cross-domain scenarios validate the efficacy of our
proposed framework for sleep staging and its advantage over state-of-the-art
UDA methods. The source code and supplementary material are available at
https://github.com/emadeldeen24/ADAST.

8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg