Progressive Identification of True Labels for Partial-Label Learning

2020-02-19 · ICML 2020 1 · Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.

📄 PDF Abstract BibTeX arXiv:2002.08053

Code (1)

Lvcrezia77/PRODEN 공식 구현 pytorch

Tasks

Partial Label Learning Stochastic OptimizationWeakly-supervised Learning

Partial-Label Regression

2023-06-15 · AAAI 2023 2 · Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang 외

Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the clas…

Partial Label Learning regression Weakly-supervised Learning

CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning

2023-03-18 · CVPR 2024 1 · Shiyu Tian, Hongxin Wei, Yiqun Wang, Lei Feng

Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth label. Identification-based methods have …

Partial Label Learning Weakly-supervised Learning

Unsupervised Person Re-identification via Multi-label Classification

2020-04-20 · CVPR 2020 6 · Dongkai Wang, Shiliang Zhang

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressi…

Classification General Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION+5

Unreliable Partial Label Learning with Recursive Separation

2023-02-20 · Yu Shi, Ning Xu, Hua Yuan, Xin Geng

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. However, the assumption that the ground-trut…

Partial Label Learning Weakly-supervised Learning

Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning

2024-02-07 · Darshana Saravanan, Naresh Manwani, Vineet Gandhi

Partial label learning (PLL) is a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels (partial label), one of which is the true label. Noisy PLL (NPLL) relaxes this c…

Partial Label Learning Pseudo Label Weakly-supervised Learning