Self-supervised learningPartial label learningMachine learningAs a main branch of weakly supervised learning paradigm, partial label learning (PLL) copes with the situation where each sample corresponds to ambiguous candidate labels containing the unknown true label. The primary difficulty of PLL lies in...
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。
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 learning aims to learn from training examples each associated with a set of candidate labels, among which only one label is valid for the tra
部分标签学习(Partial Label Learning, PLL)则是另一种弱监督学习场景,其中每个实例被赋予一个候选标签集合,但真正的标签是未知的。 “disambiguated attention embedding for multi-instance partial-label learning” 技术试图结合这两种方法的优势,通过引入消歧注意力嵌入来处理多示例部分标签学习问题。这种技术可能涉及...
As discussed in Section 2, PLL has some connections with PML, but addresses different problems. However, the proposed PML-MT model can be also applied on partial label learning problems easily by dropping the label correlations learning term. This extension leads to the following degraded version...
Paper tables with annotated results for Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set with the ground-truth label included. However, in a more practical but challenging scenario, the annotator may miss the ground-truth and provide a wrong candidate set...
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...