Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Article Swipe
Youcai Zhang
,
Yuhao Cheng
,
Xinyu Huang
,
Fei Wen
,
Rui Feng
,
Yaqian Li
,
Yandong Guo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.07368
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.07368
Multi-label learning in the presence of missing labels (MLML) is a challenging problem. Existing methods mainly focus on the design of network structures or training schemes, which increase the complexity of implementation. This work seeks to fulfill the potential of loss function in MLML without increasing the procedure and complexity. Toward this end, we propose two simple yet effective methods via robust loss design based on an observation that a model can identify missing labels during training with a high precision. The first is a novel robust loss for negatives, namely the Hill loss, which re-weights negatives in the shape of a hill to alleviate the effect of false negatives. The second is a self-paced loss correction (SPLC) method, which uses a loss derived from the maximum likelihood criterion under an approximate distribution of missing labels. Comprehensive experiments on a vast range of multi-label image classification datasets demonstrate that our methods can remarkably boost the performance of MLML and achieve new state-of-the-art loss functions in MLML.
Related Topics
Concepts
Computer science
Focus (optics)
Missing data
Simple (philosophy)
Function (biology)
Range (aeronautics)
Machine learning
Artificial intelligence
Pattern recognition (psychology)
Data mining
Algorithm
Engineering
Aerospace engineering
Physics
Optics
Philosophy
Evolutionary biology
Epistemology
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.07368
- https://arxiv.org/pdf/2112.07368
- OA Status
- green
- Cited By
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226095984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226095984Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2112.07368Digital Object Identifier
- Title
-
Simple and Robust Loss Design for Multi-Label Learning with Missing LabelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-13Full publication date if available
- Authors
-
Youcai Zhang, Yuhao Cheng, Xinyu Huang, Fei Wen, Rui Feng, Yaqian Li, Yandong GuoList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.07368Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2112.07368Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2112.07368Direct OA link when available
- Concepts
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Computer science, Focus (optics), Missing data, Simple (philosophy), Function (biology), Range (aeronautics), Machine learning, Artificial intelligence, Pattern recognition (psychology), Data mining, Algorithm, Engineering, Aerospace engineering, Physics, Optics, Philosophy, Evolutionary biology, Epistemology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 7, 2022: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Toward | 50 |
| abstract_inverted_index.design | 19, 63 |
| abstract_inverted_index.during | 75 |
| abstract_inverted_index.effect | 106 |
| abstract_inverted_index.labels | 7, 74 |
| abstract_inverted_index.mainly | 15 |
| abstract_inverted_index.namely | 90 |
| abstract_inverted_index.robust | 61, 86 |
| abstract_inverted_index.second | 111 |
| abstract_inverted_index.simple | 56 |
| abstract_inverted_index.achieve | 159 |
| abstract_inverted_index.derived | 123 |
| abstract_inverted_index.fulfill | 36 |
| abstract_inverted_index.labels. | 135 |
| abstract_inverted_index.maximum | 126 |
| abstract_inverted_index.method, | 118 |
| abstract_inverted_index.methods | 14, 59, 150 |
| abstract_inverted_index.missing | 6, 73, 134 |
| abstract_inverted_index.network | 21 |
| abstract_inverted_index.propose | 54 |
| abstract_inverted_index.without | 44 |
| abstract_inverted_index.Existing | 13 |
| abstract_inverted_index.datasets | 146 |
| abstract_inverted_index.function | 41 |
| abstract_inverted_index.identify | 72 |
| abstract_inverted_index.increase | 27 |
| abstract_inverted_index.learning | 1 |
| abstract_inverted_index.presence | 4 |
| abstract_inverted_index.problem. | 12 |
| abstract_inverted_index.schemes, | 25 |
| abstract_inverted_index.training | 24, 76 |
| abstract_inverted_index.alleviate | 104 |
| abstract_inverted_index.criterion | 128 |
| abstract_inverted_index.effective | 58 |
| abstract_inverted_index.functions | 163 |
| abstract_inverted_index.negatives | 96 |
| abstract_inverted_index.potential | 38 |
| abstract_inverted_index.procedure | 47 |
| abstract_inverted_index.complexity | 29 |
| abstract_inverted_index.correction | 116 |
| abstract_inverted_index.increasing | 45 |
| abstract_inverted_index.likelihood | 127 |
| abstract_inverted_index.negatives, | 89 |
| abstract_inverted_index.negatives. | 109 |
| abstract_inverted_index.precision. | 80 |
| abstract_inverted_index.re-weights | 95 |
| abstract_inverted_index.remarkably | 152 |
| abstract_inverted_index.self-paced | 114 |
| abstract_inverted_index.structures | 22 |
| abstract_inverted_index.Multi-label | 0 |
| abstract_inverted_index.approximate | 131 |
| abstract_inverted_index.challenging | 11 |
| abstract_inverted_index.complexity. | 49 |
| abstract_inverted_index.demonstrate | 147 |
| abstract_inverted_index.experiments | 137 |
| abstract_inverted_index.multi-label | 143 |
| abstract_inverted_index.observation | 67 |
| abstract_inverted_index.performance | 155 |
| abstract_inverted_index.distribution | 132 |
| abstract_inverted_index.Comprehensive | 136 |
| abstract_inverted_index.classification | 145 |
| abstract_inverted_index.implementation. | 31 |
| abstract_inverted_index.state-of-the-art | 161 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |