hikvision-research/SSOD: An official implementation of CVPR 2022 paper "Label Matching Semi-Supervised Object Detection". (github.com)github.com/hikvision-research/SSOD 0.摘要 随着mean teacher这样的self-training的发展,半监督目标检测取得了显著的进展。尽管取得了很好的结果,但在以往的工作中,标签错...
Semi-Supervised Learning只需要少量有带标签的样本和大量无标签的样本,然后进行监督学习,从而达到提升学习器性能的目的。 Semi-Supervised Learning利用labeled和unlabeled数据在分布上的连续性和一致性,增强模型的泛化能力。通过在损失函数中添加与unlabeled sample的正则化信息,增强学习器对未知数据的泛化能力: Semi-Supervis...
# Label Spreading fromsklearn.semi_supervisedimportLabelSpreading label_spread = LabelSpreading(kernel="knn", alpha=0.8)# 正则 label_spread.fit(X, labels) # Label Spreading打标签后的结果 output= np.asarray(label_spread.transduction_) outer_numbers = np.where(output == outer)[0] inner_numbers ...
We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing ...
In order to represent the semi-supervised or censored data, we'll need to do a little data preprocessing. First, we'll walk through a simple example, and then we'll move on to some more difficult cases: 为了准备半监督或者删减的数据,我们将需要做一些数据预处理。首先,我们将要讲述一个简单的...
Deep co-training with task decomposition for semisupervised domain adaptation一文中,将SSDA拆解为SSL和UDA任务。两个不同的子任务分别产生伪标签,并通过协同训练相互学习。 Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation一文中通过测量成对特征相似性将目标特征分组。
The task of semi-supervised partial label learning is to induce a multi-class classification model f:X↦Y from training set D. For each Label set assignment Dlsa is realized by three steps: label set assignment, reliable label confidence recovery and predictive model induction. An assignment ...
Manually labeled data-sets are vital to graph-based semisupervised learning. However, in the real world, labeled data-sets are often heavily imbalanced, and the classifiers trained on such skewed data tend to show poor performance for low-frequency classes. In this paper, we deal with an imbal...
下载地址:https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Label_Efficient_Semi-Supervised_Learning_via_Graph_Filtering_CVPR_2019_paper.pdf 2.背景动机及贡献 深度学习和神经网络的成功是以大量标记数据和长时间训练为代价的,半监督学习很重要,因为可以利用大量的无标签数据进行学习,从而大大节省人工成...
马东什么:deep semi supervised中的各类"match"(待续)11 赞同 · 0 评论文章 各类match的方法中使用的伪标签相比,这篇文章的出发点是完全不同的,fixmatch,softmatch等研究工作是认为模型的置信度越高则标签的质量越高,但如果只考虑过高的置信度的无标签样本则会导致选中的伪标签数量太少,因此需要通过各种手段来寻找...