Delving Deep into Simplicity Bias for Long-Tailed Image Recognition Article Swipe
Xiu-Shen Wei
,
Xuhao Sun
,
Yang Shen
,
Anqi Xu
,
Peng Wang
,
Faen Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.03264
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.03264
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks. In this work, we investigate SB in long-tailed image recognition and find the tail classes suffer more severely from SB, which harms the generalization performance of such underrepresented classes. We empirically report that self-supervised learning (SSL) can mitigate SB and perform in complementary to the supervised counterpart by enriching the features extracted from tail samples and consequently taking better advantage of such rare samples. However, standard SSL methods are designed without explicitly considering the inherent data distribution in terms of classes and may not be optimal for long-tailed distributed data. To address this limitation, we propose a novel SSL method tailored to imbalanced data. It leverages SSL by triple diverse levels, i.e., holistic-, partial-, and augmented-level, to enhance the learning of predictive complex patterns, which provides the potential to overcome the severe SB on tail data. Both quantitative and qualitative experimental results on five long-tailed benchmark datasets show our method can effectively mitigate SB and significantly outperform the competing state-of-the-arts.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.03264
- https://arxiv.org/pdf/2302.03264
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319653608
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319653608Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.03264Digital Object Identifier
- Title
-
Delving Deep into Simplicity Bias for Long-Tailed Image RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-07Full publication date if available
- Authors
-
Xiu-Shen Wei, Xuhao Sun, Yang Shen, Anqi Xu, Peng Wang, Faen ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.03264Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.03264Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.03264Direct OA link when available
- Concepts
-
Benchmark (surveying), Discriminative model, Generalization, Computer science, Simplicity, Artificial intelligence, Machine learning, Image (mathematics), Pattern recognition (psychology), Artificial neural network, Mathematics, Mathematical analysis, Geodesy, Epistemology, Philosophy, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.competing | 187 |
| abstract_inverted_index.enriching | 77 |
| abstract_inverted_index.extracted | 80 |
| abstract_inverted_index.favorably | 13 |
| abstract_inverted_index.leverages | 134 |
| abstract_inverted_index.partial-, | 142 |
| abstract_inverted_index.patterns, | 152 |
| abstract_inverted_index.potential | 156 |
| abstract_inverted_index.Simplicity | 0 |
| abstract_inverted_index.explicitly | 100 |
| abstract_inverted_index.holistic-, | 141 |
| abstract_inverted_index.imbalanced | 131 |
| abstract_inverted_index.outperform | 185 |
| abstract_inverted_index.phenomenon | 5 |
| abstract_inverted_index.predictive | 16, 150 |
| abstract_inverted_index.supervised | 26, 74 |
| abstract_inverted_index.considering | 101 |
| abstract_inverted_index.counterpart | 75 |
| abstract_inverted_index.distributed | 117 |
| abstract_inverted_index.effectively | 180 |
| abstract_inverted_index.empirically | 59 |
| abstract_inverted_index.investigate | 33 |
| abstract_inverted_index.limitation, | 122 |
| abstract_inverted_index.long-tailed | 36, 116, 173 |
| abstract_inverted_index.performance | 53 |
| abstract_inverted_index.qualitative | 168 |
| abstract_inverted_index.recognition | 38 |
| abstract_inverted_index.consequently | 85 |
| abstract_inverted_index.distribution | 105 |
| abstract_inverted_index.experimental | 169 |
| abstract_inverted_index.quantitative | 166 |
| abstract_inverted_index.complementary | 71 |
| abstract_inverted_index.significantly | 184 |
| abstract_inverted_index.discriminative | 27 |
| abstract_inverted_index.generalization | 52 |
| abstract_inverted_index.self-supervised | 62 |
| abstract_inverted_index.augmented-level, | 144 |
| abstract_inverted_index.underrepresented | 56 |
| abstract_inverted_index.state-of-the-arts. | 188 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |