TL;DR
SMVPU first extracts reliable negative samples from the unlabeled set and assigns similarity-weighted values to the remaining unlabeled instances, and then incorporates multi-view representations to enhance feature compatibility and discriminability.
This work proposes an integrated learning paradigm that simultaneously enhances feature compactness and improves robustness against label noise and introduces a feature disentanglement mechanism that isolates reliable label-related feature representations from spurious ones introduced by noisy supervision.
Evaluating the performance of Greedy K-center across a variety of metric spaces shows that mapping unlabeled instances into a predictive probability space and weighting the result by entropy often dominates the other options for active learning selection with Greedy K-center.
C-Score, a compact framework that evaluates training behavior in three complementary spaces: prediction, feature representation, and optimization, suggests that clean accuracy alone is insufficient for evaluating SSL robustness in open-world environments, and that internal diagnostic signals are necessary for more reli...
Existing semi-supervised learning (SSL) methods rely predominantly on pseudo-labeling and consistency regularization to leverage unlabeled data, demonstrating significant performance improvements. However, we pinpoint that these methods suffer from a confidence-for-weighting issue, overvaluing high-confidence pseudo-la...
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.
DSCF establishes a unified framework that incorporates pointwise constraints to enforce clustering consistency for labeled samples, while a hypergraph-based pairwise constraint propagation strategy is employed to transfer supervisory information to unlabeled data.
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