We call this kind of problem as "semi-supervised weak-label learning" problem. In this work we propose the SSWL (Semi-Supervised Weak-Label) method to address this problem. Both instance similarity and label similarity are considered for the complement of missing labels. Ensemble of multiple ...
Semi-supervised Learning ;半监督学习 1. 进入半监督学习2.半监督学习出现的原因??? 原因:收集样本数据容易,但是给每个样本打标签成本就很高。 Collectingdatais easy, but collecting “labelled”datais expensive. 3. 本篇博客讲解的半监督学习的内容? 3.1Semi-supervisedLearningfor ...
[6] Hao-Chen Dong, Yu-Feng Li, and Zhi-Hua Zhou. Learning from semi-supervised weak-label data. In AAAI, volume 32, 2018. [7] Baoyuan Wu, Fan Jia, Wei Liu, Bernard Ghanem, and Siwei Lyu. Multi-label learning with missing labels using mixed dependency graphs. IJCV, 126(8):875–8...
Zhang J, Li SZ, Jiang M, Tan KC (2020) Learning from weakly labeled data based on manifold regularized sparse model. IEEE Trans Cybern 52(5):3841–3854 Article Google Scholar Tan AH, Liang JY, Wu WZ, Zhang J (2022) Semi-supervised partial multi-label classification via consistency lear...
有两种主要的技术能够实现此目的,即主动学*(active learning)【2】和半监督学*(semi-supervised learning)【3-5】。 主动学*假设有一个「神谕」(oracle),比如人类专家,可以向它查询所选未标注数据的真值标签。相比之下,半监督学*试图在没有人为干预的前提下,自动利用已标注数据、以及未标注数据来提升学*性能。
—The paper introduces a novel ensemble method for semi-supervised learning. The method integrates the regularized classier based on data 1-D representation and label boosting in a serial ensemble. In each stage, the data set is rst smoothly sorted and represented as a 1-D stack, which preser...
The task of semi-supervised partial label learning is to induce a multi-class classification model f:X↦Y from training set D. For each Label set assignment Dlsa is realized by three steps: label set assignment, reliable label confidence recovery and predictive model induction. An assignment ...
To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume ...
Inspired by awesome-deep-vision, awesome-deep-learning-papers, and awesome-self-supervised-learning. Background What is Semi-Supervised Learning? It is a special form of classification. Traditional classifiers use only labeled data (feature / label pairs) to train. Labeled instances however are ...
In this semi-supervised formulation, a model is trained on labeled data and used to predict pseudo-labels for the unlabeled data. The model is then trained on both ground truth labels and pseudo-labels simultaneously. a. Pseudo-label