Denoising after Entropy-Based Debiasing a Robust Training Method for Dataset Bias with Noisy Labels Article Swipe
Sumyeong Ahn
,
Se-Young Yun
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i1.25088
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i1.25088
Improperly constructed datasets can result in inaccurate inferences. For instance, models trained on biased datasets perform poorly in terms of generalization (i.e., dataset bias). Recent debiasing techniques have successfully achieved generalization performance by underestimating easy-to-learn samples (i.e., bias-aligned samples) and highlighting difficult-to-learn samples (i.e., bias-conflicting samples). However, these techniques may fail owing to noisy labels, because the trained model recognizes noisy labels as difficult-to-learn and thus highlights them. In this study, we find that earlier approaches that used the provided labels to quantify difficulty could be affected by the small proportion of noisy labels. Furthermore, we find that running denoising algorithms before debiasing is ineffective because denoising algorithms reduce the impact of difficult-to-learn samples, including valuable bias-conflicting samples. Therefore, we propose an approach called denoising after entropy-based debiasing, i.e., DENEB, which has three main stages. (1) The prejudice model is trained by emphasizing (bias-aligned, clean) samples, which are selected using a Gaussian Mixture Model. (2) Using the per-sample entropy from the output of the prejudice model, the sampling probability of each sample that is proportional to the entropy is computed. (3) The final model is trained using existing denoising algorithms with the mini-batches constructed by following the computed sampling probability. Compared to existing debiasing and denoising algorithms, our method achieves better debiasing performance on multiple benchmarks.
Related Topics
Concepts
Debiasing
Computer science
Artificial intelligence
Noise reduction
Machine learning
Generalization
Entropy (arrow of time)
Sampling bias
Sample (material)
Pattern recognition (psychology)
Statistics
Mathematics
Sample size determination
Psychology
Mathematical analysis
Chemistry
Cognitive science
Chromatography
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v37i1.25088
- https://ojs.aaai.org/index.php/AAAI/article/download/25088/24860
- OA Status
- diamond
- Cited By
- 1
- References
- 83
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382469227
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382469227Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v37i1.25088Digital Object Identifier
- Title
-
Denoising after Entropy-Based Debiasing a Robust Training Method for Dataset Bias with Noisy LabelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-26Full publication date if available
- Authors
-
Sumyeong Ahn, Se-Young YunList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v37i1.25088Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/25088/24860Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/25088/24860Direct OA link when available
- Concepts
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Debiasing, Computer science, Artificial intelligence, Noise reduction, Machine learning, Generalization, Entropy (arrow of time), Sampling bias, Sample (material), Pattern recognition (psychology), Statistics, Mathematics, Sample size determination, Psychology, Mathematical analysis, Chemistry, Cognitive science, Chromatography, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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83Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works_count | 83 |
| abstract_inverted_index.a | 150 |
| abstract_inverted_index.In | 68 |
| abstract_inverted_index.an | 121 |
| abstract_inverted_index.as | 62 |
| abstract_inverted_index.be | 85 |
| abstract_inverted_index.by | 32, 87, 141, 194 |
| abstract_inverted_index.in | 5, 17 |
| abstract_inverted_index.is | 103, 139, 173, 178, 184 |
| abstract_inverted_index.of | 19, 91, 111, 162, 169 |
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| abstract_inverted_index.to | 52, 81, 175, 201 |
| abstract_inverted_index.we | 71, 95, 119 |
| abstract_inverted_index.(1) | 135 |
| abstract_inverted_index.(2) | 154 |
| abstract_inverted_index.(3) | 180 |
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| abstract_inverted_index.and | 39, 64, 204 |
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| abstract_inverted_index.However, | 46 |
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| abstract_inverted_index.datasets | 2, 14 |
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| abstract_inverted_index.quantify | 82 |
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| abstract_inverted_index.Improperly | 0 |
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| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.30224075 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |