A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation Article Swipe
Minghao Chen
,
Zepeng Gao
,
Shuai Zhao
,
Qibo Qiu
,
Wenxiao Wang
,
Binbin Lin
,
Xiaofei He
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.00287
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.00287
Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim to find an evaluation metric capable of assessing the quality of a transferred model without access to target validation labels. We begin with the metric based on mutual information of the model prediction. Through empirical analysis, we identify three prevalent issues with this metric: 1) It does not account for the source structure. 2) It can be easily attacked. 3) It fails to detect negative transfer caused by the over-alignment of source and target features. To address the first two issues, we incorporate source accuracy into the metric and employ a new MLP classifier that is held out during training, significantly improving the result. To tackle the final issue, we integrate this enhanced metric with data augmentation, resulting in a novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM). Additionally, we empirically demonstrate the shortcomings of previous experiment settings and conduct large-scale experiments to validate the effectiveness of our proposed metric. Furthermore, we employ our metric to automatically search for the optimal hyper-parameter set, achieving superior performance compared to manually tuned sets across four common benchmarks. Codes will be available soon.
Related Topics
Concepts
Metric (unit)
Computer science
Classifier (UML)
Domain adaptation
Set (abstract data type)
Consistency (knowledge bases)
Data mining
Machine learning
Artificial intelligence
Adaptation (eye)
Performance metric
Transfer of learning
Programming language
Management
Economics
Optics
Physics
Operations management
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.00287
- https://arxiv.org/pdf/2308.00287
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385946285
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4385946285Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.00287Digital Object Identifier
- Title
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A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain AdaptationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Minghao Chen, Zepeng Gao, Shuai Zhao, Qibo Qiu, Wenxiao Wang, Binbin Lin, Xiaofei HeList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.00287Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.00287Direct 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/2308.00287Direct OA link when available
- Concepts
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Metric (unit), Computer science, Classifier (UML), Domain adaptation, Set (abstract data type), Consistency (knowledge bases), Data mining, Machine learning, Artificial intelligence, Adaptation (eye), Performance metric, Transfer of learning, Programming language, Management, Economics, Optics, Physics, Operations managementTop 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.target | 11, 21, 52, 108 |
| abstract_inverted_index.tuning | 26 |
| abstract_inverted_index.Through | 68 |
| abstract_inverted_index.account | 83 |
| abstract_inverted_index.address | 111 |
| abstract_inverted_index.capable | 40 |
| abstract_inverted_index.conduct | 176 |
| abstract_inverted_index.domains | 12 |
| abstract_inverted_index.issues, | 115 |
| abstract_inverted_index.labeled | 20 |
| abstract_inverted_index.labels. | 14, 54 |
| abstract_inverted_index.methods | 4, 17 |
| abstract_inverted_index.metric. | 186 |
| abstract_inverted_index.metric: | 78 |
| abstract_inverted_index.optimal | 197 |
| abstract_inverted_index.quality | 44 |
| abstract_inverted_index.result. | 138 |
| abstract_inverted_index.without | 13, 49 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.accuracy | 119 |
| abstract_inverted_index.compared | 203 |
| abstract_inverted_index.enhanced | 147 |
| abstract_inverted_index.identify | 72 |
| abstract_inverted_index.manually | 205 |
| abstract_inverted_index.negative | 99 |
| abstract_inverted_index.previous | 172 |
| abstract_inverted_index.proposed | 185 |
| abstract_inverted_index.settings | 174 |
| abstract_inverted_index.superior | 201 |
| abstract_inverted_index.transfer | 7, 100 |
| abstract_inverted_index.validate | 180 |
| abstract_inverted_index.achieving | 200 |
| abstract_inverted_index.analysis, | 70 |
| abstract_inverted_index.assessing | 42 |
| abstract_inverted_index.attacked. | 93 |
| abstract_inverted_index.available | 215 |
| abstract_inverted_index.empirical | 69 |
| abstract_inverted_index.features. | 109 |
| abstract_inverted_index.improving | 136 |
| abstract_inverted_index.integrate | 145 |
| abstract_inverted_index.prevalent | 74 |
| abstract_inverted_index.resulting | 152 |
| abstract_inverted_index.training, | 134 |
| abstract_inverted_index.adaptation | 2 |
| abstract_inverted_index.classifier | 128 |
| abstract_inverted_index.evaluation | 38 |
| abstract_inverted_index.experiment | 173 |
| abstract_inverted_index.facilitate | 5 |
| abstract_inverted_index.selection. | 29 |
| abstract_inverted_index.structure. | 87 |
| abstract_inverted_index.validation | 22, 53 |
| abstract_inverted_index.Consistency | 162 |
| abstract_inverted_index.benchmarks. | 211 |
| abstract_inverted_index.demonstrate | 168 |
| abstract_inverted_index.empirically | 167 |
| abstract_inverted_index.experiments | 178 |
| abstract_inverted_index.incorporate | 117 |
| abstract_inverted_index.information | 63 |
| abstract_inverted_index.large-scale | 177 |
| abstract_inverted_index.necessitate | 18 |
| abstract_inverted_index.performance | 202 |
| abstract_inverted_index.prediction. | 67 |
| abstract_inverted_index.transferred | 47 |
| abstract_inverted_index.Augmentation | 161 |
| abstract_inverted_index.Furthermore, | 187 |
| abstract_inverted_index.Unsupervised | 0 |
| abstract_inverted_index.shortcomings | 170 |
| abstract_inverted_index.unsupervised | 156 |
| abstract_inverted_index.Additionally, | 165 |
| abstract_inverted_index.augmentation, | 151 |
| abstract_inverted_index.automatically | 193 |
| abstract_inverted_index.effectiveness | 182 |
| abstract_inverted_index.significantly | 135 |
| abstract_inverted_index.over-alignment | 104 |
| abstract_inverted_index.hyper-parameter | 25, 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |