Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning
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Reverse validation [34] is based on reverse testing [35], where a new classifier is trained on predictions on test data and evaluated on training data.
“The basic idea of reverse validation [34] is based on reverse testing [35], where a new classifier is trained on predictions on the test data and evaluated again on the training data.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [34]
Section 3.1 (page unavailable) describes training a new model on predicted labels for target-domain data, then using it to classify held-out source-domain data. But the paper calls this reverse validation. Section 6 (page unavailable) identifies reverse testing as a different technique from Fan and Davidson (2006); it does not establish that reverse validation is based on it. (LLM, conf=0.83)
Experimental — accuracy not yet measuredRCA differs from reverse validation and reverse testing in that it trains a reverse classifier on every single instance, whereas those approaches train single classifiers over the whole test set jointly.
“While the idea of RCA is similar to reverse validation [34] and reverse testing [35], the important difference is that in our approach we train a reverse classifier on every single instance while the approaches in [34], [35] train single classifiers over the whole test set and its predictions jointly to find out what the best original predictor is.”
From Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (2017) · cites this paper as [34]
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Research works printed but not linked: 14
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- Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P.V., Kawanabe, M.: Direct importance estimation with model selection and its application to covariate shift adaptation. In: NIPS '07: Proceedings of the 2007 Conference on Advances in Neu- ral Information Processing Systems, vol. 20, pp. 1433-1440. MIT Press, Cambridge (2008)
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- Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: ICML '07: Proceedings of the 24th International Conference on Machine Learning, pp. 193-200. ACM, New York (2007)
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- Fan, W., Davidson, I.: Reverse testing: an efficient framework to select amongst classifiers under sample selection bias. In: KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 147-156. ACM, New York (2006)
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Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2665165 · Cited by 152 · see citing works
Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging
Robinson, R., Valindria, V. V., Bai, W., Suzuki, H., Matthews, P. M., Page, C., Rueckert, D., & Glocker, B. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_82 · Cited by 37 · see citing works
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Cosarinsky, M., Billot, R., Mansilla, L., Jimenez, G., Gaggión, N., Fu, G., Tirer, T., & Ferrante, E. (2025) · arXiv
“Other previous studies rely on a reverse-testing strategy, where a new classifier is trained on predictions on the test data and evaluated again on the training data [25], [26], [27].”
- Authors
- Zhong, E., Fan, W., Yang, Q., Verscheure, O., & Ren, J.
- Affiliations
- Sun Yat-sen University (CN)
- IBM (United States) (US)
- Hong Kong University of Science and Technology (HK)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 547-562
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1544165511
- Cited by
- 132
- References
- 0
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5 · 2023: 7 · 2022: 14 · 2021: 14 · 2020: 11 · 2019: 25 · 2018: 9 · 2017: 10 · 2016: 3 · 2015: 7 · 2014: 6 · 2013: 1 · 2012: 6
- FWCI
- 3.36
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- transfer learning, cross-validation, domain shift, model selection, data selection, cross-domain learning, wine quality assessment, text classification
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