利用对比学习提升表示学习的效果,再利用良好的表示对label进行消歧,消歧后的label又有助于进一步生成良好的样本表征,形成良性循环,提升整体效果。 这篇文章提出的Partial label learning with COntrastive label disambiguation (PiCO) framework主要包括利用对比学习提升表示生成质量,以及基于聚类的label消歧两个核心模块。下面...
Partial-Label Learning(PLL)的问题形式如下: Partial-Label Learning的数据形式,每个样本对应一个标记候选集合,该样本的真实标记属于这个集合 简述 在处理 PLL 问题时,每个样本都与一个标签集合相关联,而正确标签包含于该集合之中。确定集合中哪个标签是正确标签是一个关键步骤。作者提出,可以通过分析模型过去的预测历史...
部分标签学习(Partial label learning, PLL)是一个重要的问题,PLL允许模型使用粗糙的标注数据集进行训练学习。在这些标注数据集中,往往没有一个明确的答案,而是有一个候选标签集合。许多神经网络的成功往往依赖于大量的高质量的标注数据,但是这意味着高成本。并且人工标注的数据本来就大部分会具有一定的固有标签模糊性和...
Partial Label LearningRegret boundPartial label learning (PLL) is a weakly supervised learning framework which learns from the data where each example is associated with a set of candidate labels, among which only one is correct. Most existing approaches are based on the disambiguation strategy, ...
3.3 SYNERGY BETWEEN CONTRASTIVE LEARNING AND LABEL DISAMBIGUATION 虽然看似彼此分离,但PiCO的两个关键组件以协作的方式工作。 由于对比术语在embedding space中有利地表现出聚类效应,标签消歧模块通过设置更精确的原型进一步利用了这一点。 一组经过精心修饰的标签消歧结果可能反过来也会影响正集结构,正集结构是对比学习...
该论文主要研究的是partial label learning(PLL)问题。该问题可以定义为如下:首先给定 为输入空间, 为输出标签空间。考虑如下的训练数据集 ,每一个元组由一张图片 和一个候选的标签集合 组成。依照监督学习任务的设定来讲,PLL的目标是通过关联样本来学习一个可以预测正确标签的映射函数。二者之间的不同点是,PLL的建...
论文翻译 —— Disambiguation-Free Partial Label Learning 非消歧偏标记学习(PL-ECOC),标题:Disambiguation-FreePartialLabelLearning文章链接:http://aaai.偏标签学习4.实验4.1实验计划.
Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate label equally or identifying the...
Partial label learning is a scenario where only a subset of the data is labeled, while the remaining data is unlabeled. In this context, detecting noise in the partial labels becomes crucial to ensure the quality and accuracy of the learned model. Here are some approaches to detect noise in...
Partial label learning deals with the problem where each training example is represented by a feature vector while associated with a set of candidate labels, among which only one label is valid. To learn from such ambiguous labeling information, the key is to try to disambiguate the candidate la...