Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Acknowledging the Unknown for Multi-label Learning with Single Positive
  Labels   Due to the difficulty of collecting exhaustive multi-label annotations,
multi-label training data often contains partial labels. We consider an extreme
of this problem, called single positive multi-label learning (SPML), where each
multi-label training image has only one positive label. Traditionally, all
unannotated labels are assumed as negative labels in SPML, which would
introduce false negative labels and make model training be dominated by assumed
negative labels. In this work, we choose to treat all unannotated labels from a
different perspective, \textit{i.e.} acknowledging they are unknown. Hence, we
propose entropy-maximization (EM) loss to maximize the entropy of predicted
probabilities for all unannotated labels. Considering the positive-negative
label imbalance of unannotated labels, we propose asymmetric pseudo-labeling
(APL) with asymmetric-tolerance strategies and a self-paced procedure to
provide more precise supervision. Experiments show that our method
significantly improves performance and achieves state-of-the-art results on all
four benchmarks.

26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int