LDIBR performs instance-adaptive imputation conditioned on instance features and the binary observation mask, and learns a prior, a reliability-gated correction, and entry-wise fusion weights to produce a normalized imputed distribution.
This work proposes ComRank, a ranking loss framework for MLCLL, which encourages complementary labels to be ranked lower than non-complementary ones, thereby modeling pairwise label relationships and ensures Bayes consistency under both uniform and biased cases.
Jin Zhu, Yi Gao, Miao Xu et al.· Neural Information Processin...· 0 citations
A novel semi-supervised framework that disentangles pseudo-label generation from the classification task via designing a dedicated pseudo-label generator to align the class distributions between labeled and unlabeled data is proposed.
Yifan Wang, Biao Liu, Xin Geng et al.· Frontiers of Computer Scienc...· 0 citations
DPCL was leveraged to learn better representation distributions by uniformly projecting data on a hypersphere, and by which more accurate pseudo labels could be generated, and achieves consistent improvements over existing state-of-the-art methods across multiple benchmarks.
Pengfei Lv, Jing Chai· International Conference on...· 0 citations
DAPR achieves the highest average Accuracy and Macro-F1 among the compared methods and obtains strong aggregate tail-class performance, particularly for Tooth Discoloration and Ulcers, indicating that DAPR improves aggregate class-balanced learning under the evaluated dataset, annotation ratio, and backbone configurati...
Xuesheng Bian, Zeyu Xie, Yuhan Sun et al.· Electronics· 0 citations
Gaussian-mixture calculations and a medical diagnosis example illustrate how uncertainty-dependent labeling mechanisms can improve estimation and classification under a fixed labeling budget.
You‐Gan Wang, Jin-Ran Wu, Geoffrey J. McLachlan· 0 citations
This review consolidates the landscape of CP adaptations for MLL under a unified framework, examining the types of outputs and guarantees they provide, where label dependencies are incorporated, and how inference cost scales with the number of labels.
