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  • Uncertainty-aware Long-tailed Weights Model the Utility of . . .
    Concretely, the advantage of the long-tailed weights ensures that even unreliable pseudo-labels still contribute to enhancing the model's robustness Besides, UES is lightweight and architecture-agnostic, easily extending to various computer vision tasks, including classification and regression
  • A novel weighted pseudo-labeling framework based on matrix . . .
    To effectively address the problem of sparsity, we proposed a novel weighted pseudo-labeling framework that mines potential unknown drug-ADR pairs by integrating multiple weighted matrix factorization (MF) models and treating them as pseudo-labeled drug-ADR pairs
  • Domain knowledge guided pseudo-label generation framework for . . .
    This is because the pseudo-labels output by the shared classifier contain a lot of noise Second, a mask matrix is generated using the domain information vector of each domain to modulate the shared classifier, improving the quality of pseudo-label generation for each domain The M2 classification accuracy is improved by 23 25% and 10%
  • Weighting Pseudo-labels via High-Activation Feature Index . . .
    Our Per-pixel Learning Weight shows that the weight on unreliable high-confidence pseudo-labels (dotted white box) is reduced in contrast to conventional confidence thresholding (≥ 0 95) Pseudo-labels are generated using AugSeg [66] after 50 epochs for 1 Pascal VOC Dataset
  • Multi-Perspective Pseudo-Label Generation and Confidence . . .
    Our pseudo-label generation method shows superior suitability for semi-supervised semantic segmentation compared to other approaches Second, we propose a confidence-weighted training method to alleviate performance degradation caused by unstable pixels
  • Debiased Learning from Naturally Imbalanced Pseudo-Labels
    We propose a novel and effective debiased learning method with pseudo-labels, based on counterfactual rea-soning and adaptive margins: The former removes the clas-sifier response bias, whereas the latter adjusts the margin of each class according to the imbalance of pseudo-labels
  • Better pseudo-label: Joint domain-aware label and dual . . .
    With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels


















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