Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes
  and Model Accuracy   A novel semi-supervised learning technique is introduced based on a simple
iterative learning cycle together with learned thresholding techniques and an
ensemble decision support system. State-of-the-art model performance and
increased training data volume are demonstrated, through the use of unlabelled
data when training deeply learned classification models. Evaluation of the
proposed approach is performed on commonly used datasets when evaluating
semi-supervised learning techniques as well as a number of more challenging
image classification datasets (CIFAR-100 and a 200 class subset of ImageNet).

15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga