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- W4297899881 abstract "Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods. Code and data are available at: https://github.com/hbzju/SoLar ." @default.
- W4297899881 created "2022-10-01" @default.
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- W4297899881 date "2022-09-21" @default.
- W4297899881 modified "2023-09-26" @default.
- W4297899881 title "SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning" @default.
- W4297899881 doi "https://doi.org/10.48550/arxiv.2209.10365" @default.
- W4297899881 hasPublicationYear "2022" @default.
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