539 citations · 703 across the 32 of their papers we have counts for
12 papers · 1 filter
Can Class-Priors Help Single-Positive Multi-Label Learning?
Biao Liu, Ning Xu, Jie Wang +1
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label. Exi…
Robust Representation Learning for Unreliable Partial Label Learning
Yu Shi, Dong-Dong Wu, Xin Geng +1
Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. How…
Exploiting Multi-Label Correlation in Label Distribution Learning
Zhiqiang Kou jing wang yuheng jia xin geng
Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in…
Variational Label-Correlation Enhancement for Congestion Prediction
Biao Liu, Congyu Qiao, Ning Xu +3
The physical design process of large-scale designs is a time-consuming task, often requiring hours to days to complete, with routing being the most critical and complex step. As th…
NormAUG: Normalization-guided Augmentation for Domain Generalization
Lei Qi, Hongpeng Yang, Yinghuan Shi +1
Deep learning has made significant advancements in supervised learning. However, models trained in this setting often face challenges due to domain shift between training and test…
Generalizable Metric Network for Cross-domain Person Re-identification
Lei Qi, Ziang Liu, Yinghuan Shi +1
Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain genera…
Towards Effective Visual Representations for Partial-Label Learning
Shiyu Xia, Jiaqi Lv, Ning Xu +2
Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning…
Patch-aware Batch Normalization for Improving Cross-domain Robustness
Lei Qi, Dongjia Zhao, Yinghuan Shi +1
Despite the significant success of deep learning in computer vision tasks, cross-domain tasks still present a challenge in which the model's performance will degrade when the train…
Data Augmentation For Label Enhancement
Zhiqiang Kou, Yuheng Jia, Jing Wang +2
Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless…
Learning From Biased Soft Labels
Hua Yuan, Ning Xu, Yu Shi +2
Knowledge distillation has been widely adopted in a variety of tasks and has achieved remarkable successes. Since its inception, many researchers have been intrigued by the dark kn…
Inaccurate Label Distribution Learning
Zhiqiang Kou, Yuheng Jia, Jing Wang +1
Label distribution learning (LDL) trains a model to predict the relevance of a set of labels (called label distribution (LD)) to an instance. The previous LDL methods all assumed t…
Unreliable Partial Label Learning with Recursive Separation
Yu Shi, Ning Xu, Hua Yuan +1
Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. Howe…