Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GALAXY: Graph-based Active Learning at the Extreme   Active learning is a label-efficient approach to train highly effective
models while interactively selecting only small subsets of unlabelled data for
labelling and training. In "open world" settings, the classes of interest can
make up a small fraction of the overall dataset -- most of the data may be
viewed as an out-of-distribution or irrelevant class. This leads to extreme
class-imbalance, and our theory and methods focus on this core issue. We
propose a new strategy for active learning called GALAXY (Graph-based Active
Learning At the eXtrEme), which blends ideas from graph-based active learning
and deep learning. GALAXY automatically and adaptively selects more
class-balanced examples for labeling than most other methods for active
learning. Our theory shows that GALAXY performs a refined form of uncertainty
sampling that gathers a much more class-balanced dataset than vanilla
uncertainty sampling. Experimentally, we demonstrate GALAXY's superiority over
existing state-of-art deep active learning algorithms in unbalanced vision
classification settings generated from popular datasets.

31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an