Weighted Ensemble Self-Supervised Learning Article Swipe
Yangjun Ruan
,
Saurabh Singh
,
Warren R. Morningstar
,
Alexander A. Alemi
,
Sergey Ioffe
,
Ian Fischer
,
Joshua V. Dillon
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.09981
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.09981
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art few-shot and supervised learning performance. In this paper, we explore how ensemble methods can improve recent SSL techniques by developing a framework that permits data-dependent weighted cross-entropy losses. We refrain from ensembling the representation backbone; this choice yields an efficient ensemble method that incurs a small training cost and requires no architectural changes or computational overhead to downstream evaluation. The effectiveness of our method is demonstrated with two state-of-the-art SSL methods, DINO (Caron et al., 2021) and MSN (Assran et al., 2022). Our method outperforms both in multiple evaluation metrics on ImageNet-1K, particularly in the few-shot setting. We explore several weighting schemes and find that those which increase the diversity of ensemble heads lead to better downstream evaluation results. Thorough experiments yield improved prior art baselines which our method still surpasses; e.g., our overall improvement with MSN ViT-B/16 is 3.9 p.p. for 1-shot learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.09981
- https://arxiv.org/pdf/2211.09981
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309584002
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309584002Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.09981Digital Object Identifier
- Title
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Weighted Ensemble Self-Supervised LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-18Full publication date if available
- Authors
-
Yangjun Ruan, Saurabh Singh, Warren R. Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. DillonList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.09981Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2211.09981Direct 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/2211.09981Direct OA link when available
- Concepts
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Computer science, Boosting (machine learning), Machine learning, Artificial intelligence, Weighting, Robustness (evolution), Ensemble learning, Supervised learning, Entropy (arrow of time), Artificial neural network, Medicine, Quantum mechanics, Physics, Radiology, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ViT-B/16 | 168 |
| abstract_inverted_index.boosting | 9 |
| abstract_inverted_index.ensemble | 42, 71, 142 |
| abstract_inverted_index.few-shot | 31, 126 |
| abstract_inverted_index.improved | 153 |
| abstract_inverted_index.increase | 138 |
| abstract_inverted_index.learning | 22, 34 |
| abstract_inverted_index.methods, | 101 |
| abstract_inverted_index.multiple | 118 |
| abstract_inverted_index.overhead | 86 |
| abstract_inverted_index.powerful | 6 |
| abstract_inverted_index.requires | 80 |
| abstract_inverted_index.results. | 149 |
| abstract_inverted_index.setting. | 127 |
| abstract_inverted_index.training | 77 |
| abstract_inverted_index.weighted | 56 |
| abstract_inverted_index.backbone; | 65 |
| abstract_inverted_index.baselines | 156 |
| abstract_inverted_index.diversity | 140 |
| abstract_inverted_index.efficient | 70 |
| abstract_inverted_index.framework | 52 |
| abstract_inverted_index.learning. | 18, 174 |
| abstract_inverted_index.technique | 7 |
| abstract_inverted_index.unlabeled | 27 |
| abstract_inverted_index.weighting | 131 |
| abstract_inverted_index.Ensembling | 0 |
| abstract_inverted_index.developing | 50 |
| abstract_inverted_index.downstream | 88, 147 |
| abstract_inverted_index.ensembling | 62 |
| abstract_inverted_index.evaluation | 119, 148 |
| abstract_inverted_index.leveraging | 25 |
| abstract_inverted_index.robustness | 15 |
| abstract_inverted_index.supervised | 17, 33 |
| abstract_inverted_index.surpasses; | 161 |
| abstract_inverted_index.techniques | 48 |
| abstract_inverted_index.estimation, | 13 |
| abstract_inverted_index.evaluation. | 89 |
| abstract_inverted_index.experiments | 151 |
| abstract_inverted_index.improvement | 165 |
| abstract_inverted_index.outperforms | 115 |
| abstract_inverted_index.uncertainty | 12 |
| abstract_inverted_index.ImageNet-1K, | 122 |
| abstract_inverted_index.demonstrated | 96 |
| abstract_inverted_index.particularly | 123 |
| abstract_inverted_index.performance, | 11 |
| abstract_inverted_index.performance. | 35 |
| abstract_inverted_index.architectural | 82 |
| abstract_inverted_index.computational | 85 |
| abstract_inverted_index.cross-entropy | 57 |
| abstract_inverted_index.effectiveness | 91 |
| abstract_inverted_index.data-dependent | 55 |
| abstract_inverted_index.representation | 64 |
| abstract_inverted_index.self-supervised | 21 |
| abstract_inverted_index.state-of-the-art | 30, 99 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |