Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Class-Balanced Active Learning for Image Classification   Active learning aims to reduce the labeling effort that is required to train
algorithms by learning an acquisition function selecting the most relevant data
for which a label should be requested from a large unlabeled data pool. Active
learning is generally studied on balanced datasets where an equal amount of
images per class is available. However, real-world datasets suffer from severe
imbalanced classes, the so called long-tail distribution. We argue that this
further complicates the active learning process, since the imbalanced data pool
can result in suboptimal classifiers. To address this problem in the context of
active learning, we proposed a general optimization framework that explicitly
takes class-balancing into account. Results on three datasets showed that the
method is general (it can be combined with most existing active learning
algorithms) and can be effectively applied to boost the performance of both
informative and representative-based active learning methods. In addition, we
showed that also on balanced datasets our method generally results in a
performance gain.

22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph