Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Multi-label Dataset Generation from Web Data   This paper presents a system towards the generation of multi-label datasets
from web data in an unsupervised manner. To achieve this objective, this work
comprises two main contributions, namely: a) the generation of a low-noise
unsupervised single-label dataset from web-data, and b) the augmentation of
labels in such dataset (from single label to multi label). The generation of a
single-label dataset uses an unsupervised noise reduction phase (clustering and
selection of clusters using anchors) obtaining a 85% of correctly labeled
images. An unsupervised label augmentation process is then performed to assign
new labels to the images in the dataset using the class activation maps and the
uncertainty associated with each class. This process is applied to the dataset
generated in this paper and a public dataset (Places365) achieving a 9.5% and
27% of extra labels in each dataset respectively, therefore demonstrating that
the presented system can robustly enrich the initial dataset.

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The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
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The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga