Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Highly Efficient Representation and Active Learning Framework for
  Imbalanced Data and its Application to COVID-19 X-Ray Classification   We propose a highly data-efficient classification and active learning
framework for classifying chest X-rays. It is based on (1) unsupervised
representation learning of a Convolutional Neural Network and (2) the Gaussian
Process method. The unsupervised representation learning employs
self-supervision that does not require class labels, and the learned features
are proven to achieve label-efficient classification. GP is a kernel-based
Bayesian approach that also leads to data-efficient predictions with the added
benefit of estimating each decision's uncertainty. Our novel framework combines
these two elements in sequence to achieve highly data and label efficient
classifications. Moreover, both elements are less sensitive to the prevalent
and challenging class imbalance issue, thanks to the (1) feature learned
without labels and (2) the Bayesian nature of GP. The GP-provided uncertainty
estimates enable active learning by ranking samples based on the uncertainty
and selectively labeling samples showing higher uncertainty. We apply this
novel combination to the data-deficient and severely imbalanced case of
COVID-19 chest X-ray classification. We demonstrate that only $\sim 10\%$ of
the labeled data is needed to reach the accuracy from training all available
labels. Its application to the COVID-19 data in a fully supervised
classification scenario shows that our model, with a generic ResNet backbone,
outperforms (COVID-19 case by 4\%) the state-of-the-art model with a highly
tuned architecture. Our model architecture and proposed framework are general
and straightforward to apply to a broader class of datasets, with expected
success.

18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol