MCDAL: Maximum Classifier Discrepancy for Active Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Jae Won | - |
dc.contributor.author | Kim, Dong-Jin | - |
dc.contributor.author | Jung, Yunjae | - |
dc.contributor.author | Kweon, In So | - |
dc.date.accessioned | 2023-07-24T09:21:25Z | - |
dc.date.available | 2023-07-24T09:21:25Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2022-03 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/187296 | - |
dc.description.abstract | Recent state-of-the-art active learning methods have mostly leveraged generative adversarial networks (GANs) for sample acquisition; however, GAN is usually known to suffer from instability and sensitivity to hyperparameters. In contrast to these methods, in this article, we propose a novel active learning framework that we call Maximum Classifier Discrepancy for Active Learning (MCDAL) that takes the prediction discrepancies between multiple classifiers. In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them. Intuitively, the discrepancies in the auxiliary classification layers' predictions indicate the uncertainty in the prediction. In this regard, we propose a novel method to leverage the classifier discrepancies for the acquisition function for active learning. We also provide an interpretation of our idea in relation to existing GAN-based active learning methods and domain adaptation frameworks. Moreover, we empirically demonstrate the utility of our approach where the performance of our approach exceeds the state-of-the-art methods on several image classification and semantic segmentation datasets in active learning setups. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | MCDAL: Maximum Classifier Discrepancy for Active Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Jin | - |
dc.identifier.doi | 10.1109/TNNLS.2022.3152786 | - |
dc.identifier.scopusid | 2-s2.0-85127059203 | - |
dc.identifier.wosid | 000777270000001 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, pp.1 - 11 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 11 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Classification (of information) | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Job analysis | - |
dc.subject.keywordPlus | Semantic Segmentation | - |
dc.subject.keywordPlus | Semantics | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Learning systems | - |
dc.subject.keywordAuthor | Generative adversarial networks | - |
dc.subject.keywordAuthor | Semantics | - |
dc.subject.keywordAuthor | Generators | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Active learning | - |
dc.subject.keywordAuthor | classifier discrepancy | - |
dc.subject.keywordAuthor | data issues | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | visual recognition | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9739135 | - |
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