To this end, this paper proposes a novel framework called Debiased Graph Contrastive Learning Based on Positive and Unlabeled Learning (DGCL-PU). Firstly, in this framework, we cluster the nodes by using the K-means algorithm and then treat the samples that are the same as the anchor as ...
为了消除这样的偏差,文中将来自\bar{N}_t 的样本定义为未标记的样本,而非负样本,然后利用Positive-Unlabeled (PU) learning去准确测量损失。实际上,即使邻域内的样本都相似,也不能假设该区域外的样本必然不同。例如,在存在长期季节性的情况下,信号可以在遥远的时间表现出类似的特性。 【PU learning】在PU ...
hard negative sample困难负样本采样:≠负样本,,但算是负采样(negative sampleing)的一种特定类型。 Positive-Unlabeled learning:又称为Positive-Instance based Learning (PIL),通常用于处理在有限标记数据集中分类非常罕见的类别,或者构建二元分类器时面临未标记样本的情况。与标准的监督式学习任务不同,PU学习只提供关于...
Contrastive learning is a discriminative representation learning framework in computer science that aims to train a feature extractor without the need for labels. It involves minimizing the distance between positive examples and anchor examples, while maximizing the distance between negative examples and anc...
Note that a pseudo-label of a strongly augmented sample u′ is defined by the label prediction on the unlabeled sample u before strong augmentation. The positive sample pairs represent the samples whose pseudo-labels are the same, and the aug- mented samples in Pˆ(u′...
Here, we pre-train the feature encoder on our entire unlabeled training set, and then learn the classifier and fine-tune the encoder using a subset of labeled images. Figure 6 (orange curve) shows the results. In contrast to the model trained from scratch (blue curve), learning the ...
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Reconstruction-driven contrastive learning for unsupervised skeleton-based human action recognition 来自 ACM 喜欢 0 阅读量: 2 作者:X Liu,B Gao 摘要: At present, researchers intend to use unlabeled skeleton data for human action recognition considering the cumbersome process of annotating large-scale ...
在刚刚结束的NeurIPS 2023，有一个4th Workshop on Self-Supervised Learning: Theory and Practice【web...
一、简介对比学习(Contrastive Learning)是一种自监督学习(self-Supervised Learning)方法,它通过比较样本对来学习数据的表示。对比学习的核心思想是,相似的样本应该在表示空间中彼此靠近,而不相似的样本应该彼…