Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Noisy Labels for Weakly Supervised Gamma Hadron Classification   Gamma hadron classification, a central machine learning task in gamma ray
astronomy, is conventionally tackled with supervised learning. However, the
supervised approach requires annotated training data to be produced in
sophisticated and costly simulations. We propose to instead solve gamma hadron
classification with a noisy label approach that only uses unlabeled data
recorded by the real telescope. To this end, we employ the significance of
detection as a learning criterion which addresses this form of weak
supervision. We show that models which are based on the significance of
detection deliver state-of-the-art results, despite being exclusively trained
with noisy labels; put differently, our models do not require the costly
simulated ground-truth labels that astronomers otherwise employ for classifier
training. Our weakly supervised models exhibit competitive performances also on
imbalanced data sets that stem from a variety of other application domains. In
contrast to existing work on class-conditional label noise, we assume that only
one of the class-wise noise rates is known.

8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess