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...
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 ...
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...
Wu M, Pan S, Du L, Zhu X (2021) Learning graph neural networks with positive and unlabeled nodes. ACM Trans Knowl Disc Data (TKDD) 15(6):1–25 Article Google Scholar Kale R, Thing VLL (2023) Few-shot weakly-supervised cybersecurity anomaly detection. Comput Secur 130:103194 Gao Y,...
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 ...
在本文中,微软亚洲研究院的研究员和实习生们提出了一个简单且高效的无监督预训练方法——参数化实例分类(PIC)。和目前最常用的非参数化对比学习方法不同,PIC 采用了类似于有监督图片分类的框架,将每个实例或图片看作一个独立的类别进行实例分类从而进行无监督学习。与 SimCLR 或 MoCo 这类方法相比,PIC 不需要处理...
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此远离。可以应用到CV、NLP等领域。 二、对比学习 1.MOCO ...
对比学习（Contrastive Learning）是近年来深度学习领域中的一个热点研究方向，尤其在自监督学习中显示出了...
Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from limited labeled examples. We extend this paradigm to the classical positiv