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- W4386973025 abstract "Openset semi-supervised learning (OSSL) has been mainly used in recent years to address the negative impact of out-of-distribution (OOD) unlabeled data on semi-supervised learning. However, current openset semi-supervised learning approaches directly discard the identified ood data, while ignoring the positive impact of ood data similar to in-distribution (ID) data on semi-supervised learning. And the method based on learnable parameters is prone to overfitting ood unlabeled data. Therefore, we propose a prototype-based OSSL framework for multi-information mining (PSML) to better mine ood unlabeled data and improve the classification performance of ID data. Specifically, we detect the multi-level information density of unlabeled data by a learnable OOD detector and a non-learnable prototype similarity detector. Then we perform different degrees of prototype based information mining on the data with different information densities (ID data, similar data, and dissimilar data). As a result, PSML is able to perform better information mining in ood unlabeled data, and thus it achieves a significant performance improvement relative to previous work, as demonstrated in experiments on multiple benchmark datasets." @default.
- W4386973025 created "2023-09-23" @default.
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- W4386973025 date "2023-01-01" @default.
- W4386973025 modified "2023-09-29" @default.
- W4386973025 title "PSML: Prototype-Based OSSL Framework for Multi-information Mining" @default.
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- W4386973025 doi "https://doi.org/10.1007/978-3-031-44201-8_18" @default.
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