Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimax Active Learning   Active learning aims to develop label-efficient algorithms by querying the
most representative samples to be labeled by a human annotator. Current active
learning techniques either rely on model uncertainty to select the most
uncertain samples or use clustering or reconstruction to choose the most
diverse set of unlabeled examples. While uncertainty-based strategies are
susceptible to outliers, solely relying on sample diversity does not capture
the information available on the main task. In this work, we develop a
semi-supervised minimax entropy-based active learning algorithm that leverages
both uncertainty and diversity in an adversarial manner. Our model consists of
an entropy minimizing feature encoding network followed by an entropy
maximizing classification layer. This minimax formulation reduces the
distribution gap between the labeled/unlabeled data, while a discriminator is
simultaneously trained to distinguish the labeled/unlabeled data. The highest
entropy samples from the classifier that the discriminator predicts as
unlabeled are selected for labeling. We evaluate our method on various image
classification and semantic segmentation benchmark datasets and show superior
performance over the state-of-the-art methods.

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The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an