DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability Article Swipe
Skylar E. Stolte
,
Kyle Volle
,
Aprinda Indahlastari
,
Alejandro Albizu
,
Adam J. Woods
,
Kevin Brink
,
Matthew Hale
,
Ruogu Fang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.10453
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.10453
Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++'s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.10453
- https://arxiv.org/pdf/2308.10453
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386081433
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4386081433Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.10453Digital Object Identifier
- Title
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DOMINO++: Domain-aware Loss Regularization for Deep Learning GeneralizabilityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-21Full publication date if available
- Authors
-
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, Ruogu FangList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.10453Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2308.10453Direct 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/2308.10453Direct OA link when available
- Concepts
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Generalizability theory, Computer science, Artificial intelligence, Test data, Regularization (linguistics), Machine learning, Probabilistic logic, Synthetic data, Domino, Pattern recognition (psychology), Mathematics, Statistics, Catalysis, Chemistry, Programming language, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.compare | 159 |
| abstract_inverted_index.designs | 103, 147 |
| abstract_inverted_index.dynamic | 119, 149 |
| abstract_inverted_index.focused | 123 |
| abstract_inverted_index.imposed | 139 |
| abstract_inverted_index.improve | 74, 97, 210 |
| abstract_inverted_index.model's | 24 |
| abstract_inverted_index.perform | 30 |
| abstract_inverted_index.propose | 114 |
| abstract_inverted_index.rotated | 187 |
| abstract_inverted_index.scaling | 142, 150 |
| abstract_inverted_index.scanner | 198 |
| abstract_inverted_index.serious | 5 |
| abstract_inverted_index.(DOMINO) | 95 |
| abstract_inverted_index.DOMINO++ | 127, 146, 160 |
| abstract_inverted_index.Previous | 89 |
| abstract_inverted_index.adaptive | 154 |
| abstract_inverted_index.baseline | 165 |
| abstract_inverted_index.classes. | 88 |
| abstract_inverted_index.clinical | 218 |
| abstract_inverted_index.consists | 14, 182 |
| abstract_inverted_index.features | 66, 80 |
| abstract_inverted_index.learning | 10, 79 |
| abstract_inverted_index.magnetic | 172 |
| abstract_inverted_index.outputs. | 69 |
| abstract_inverted_index.proposed | 91 |
| abstract_inverted_index.reliable | 48 |
| abstract_inverted_index.separate | 201 |
| abstract_inverted_index.spurious | 59 |
| abstract_inverted_index.struggle | 37 |
| abstract_inverted_index.superior | 204 |
| abstract_inverted_index.training | 25 |
| abstract_inverted_index.DOMINO++, | 115 |
| abstract_inverted_index.challenge | 6 |
| abstract_inverted_index.datasets, | 188 |
| abstract_inverted_index.decreases | 55 |
| abstract_inverted_index.different | 21, 196 |
| abstract_inverted_index.essential | 45 |
| abstract_inverted_index.in-domain | 33 |
| abstract_inverted_index.knowledge | 132 |
| abstract_inverted_index.potential | 208 |
| abstract_inverted_index.resonance | 173 |
| abstract_inverted_index.synthetic | 184 |
| abstract_inverted_index.DOMINO++'s | 203 |
| abstract_inverted_index.Overcoming | 41 |
| abstract_inverted_index.calibrated | 71 |
| abstract_inverted_index.deployment | 49, 213 |
| abstract_inverted_index.indicative | 84 |
| abstract_inverted_index.integrates | 128 |
| abstract_inverted_index.respective | 87 |
| abstract_inverted_index.calibration | 54, 94 |
| abstract_inverted_index.connections | 60 |
| abstract_inverted_index.data-guided | 131 |
| abstract_inverted_index.discrepancy | 43 |
| abstract_inverted_index.evaluations | 158 |
| abstract_inverted_index.performance | 205 |
| abstract_inverted_index.trustworthy | 212 |
| abstract_inverted_index.calibration, | 99 |
| abstract_inverted_index.demonstrates | 206 |
| abstract_inverted_index.domain-aware | 92, 120 |
| abstract_inverted_index.segmentation | 170 |
| abstract_inverted_index.Comprehensive | 157 |
| abstract_inverted_index.dual-guidance | 117 |
| abstract_inverted_index.expert-guided | 129 |
| abstract_inverted_index.significantly | 20 |
| abstract_inverted_index.generalization | 2, 76 |
| abstract_inverted_index.regularization | 122, 144, 155 |
| abstract_inverted_index.regularization. | 135 |
| abstract_inverted_index.generalizability | 106 |
| abstract_inverted_index.generalizability. | 126 |
| abstract_inverted_index.Out-of-distribution | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |