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...
Partial Label Learning(PLL)问题也是预测一个样本对应的label,但是和有监督学习问题的差异是,PLL问题的训练数据中,一个输入样本对应多个候选label,真正的label是候选label中的一个。 为什么会有PLL这样的问题呢?因为在现实问题中,label来自于人工标注,而有的样本人工标注比较困难,只标注一个label会造成噪声较大的问题。
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...
Competitive learningGraph neural networkPartial Label Learning (PLL) is a weakly supervised learning framework where each instance may be associated with more than one candidate label, among which only one is true. Traditionally, the PLL problem is solved by removing the false candidate labels based...
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...
Partial 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, which either identifies the valid ...
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient...
Partial 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, which either identifies the valid ...
Partial 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, which either identifies the valid ...