Learning Deep Networks from Noisy Labels with Dropout Regularization Article Swipe
Ishan Jindal
,
Matthew Nokleby
,
Xuewen Chen
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.03419
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.03419
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.
Related Topics
Concepts
Softmax function
Computer science
Dropout (neural networks)
Artificial intelligence
Regularization (linguistics)
MNIST database
Deep neural networks
Deep learning
Artificial neural network
Noise (video)
Machine learning
Stochastic gradient descent
Perceptron
Pattern recognition (psychology)
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1705.03419
- https://arxiv.org/pdf/1705.03419
- OA Status
- green
- Cited By
- 19
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2583188830
All OpenAlex metadata
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https://openalex.org/W2583188830Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1705.03419Digital Object Identifier
- Title
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Learning Deep Networks from Noisy Labels with Dropout RegularizationWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2017Year of publication
- Publication date
-
2017-05-09Full publication date if available
- Authors
-
Ishan Jindal, Matthew Nokleby, Xuewen ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/1705.03419Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1705.03419Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1705.03419Direct OA link when available
- Concepts
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Softmax function, Computer science, Dropout (neural networks), Artificial intelligence, Regularization (linguistics), MNIST database, Deep neural networks, Deep learning, Artificial neural network, Noise (video), Machine learning, Stochastic gradient descent, Perceptron, Pattern recognition (psychology), Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 1, 2022: 2, 2021: 3Per-year citation counts (last 5 years)
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12Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.datasets | 1, 21, 118 |
| abstract_inverted_index.gradient | 71 |
| abstract_inverted_index.learning | 89 |
| abstract_inverted_index.methods. | 127 |
| abstract_inverted_index.obtained | 8 |
| abstract_inverted_index.proposed | 122 |
| abstract_inverted_index.standard | 45 |
| abstract_inverted_index.training | 38 |
| abstract_inverted_index.versions | 112 |
| abstract_inverted_index.Numerical | 108 |
| abstract_inverted_index.augmented | 79 |
| abstract_inverted_index.effective | 30 |
| abstract_inverted_index.encourage | 87 |
| abstract_inverted_index.networks. | 41 |
| abstract_inverted_index.technique | 31, 124 |
| abstract_inverted_index.training. | 107 |
| abstract_inverted_index.Mechanical | 11 |
| abstract_inverted_index.accounting | 33 |
| abstract_inverted_index.end-to-end | 69 |
| abstract_inverted_index.mislabeled | 20 |
| abstract_inverted_index.stochastic | 70 |
| abstract_inverted_index.unreliable | 4 |
| abstract_inverted_index.classifiers | 17 |
| abstract_inverted_index.experiments | 109 |
| abstract_inverted_index.labels-such | 5 |
| abstract_inverted_index.mislabeled) | 76 |
| abstract_inverted_index.non-trivial | 92 |
| abstract_inverted_index.outperforms | 125 |
| abstract_inverted_index.statistics. | 57 |
| abstract_inverted_index.performance. | 25 |
| abstract_inverted_index.platforms-and | 16 |
| abstract_inverted_index.regularization | 98 |
| abstract_inverted_index.overdetermined, | 82 |
| abstract_inverted_index.state-of-the-art | 126 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile |