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...
# 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 ...
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...
Noisy label learningSemi-supervisedAdversarial trainingThis paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need to identify the entity boundary ...
半监督学习(Semi-Supervised Learning)在标记数据稀缺的情况下,表现出了卓越的性能。这种学习方法结合了监督学习与无监督学习的优点,能够在只有少量标记数据和大量未标记数据的情况下,使模型变得更加强大。例如,在医疗影像分析与语音识别等领域,通常只存在少量标记样本,因而半监督学习成为了解决此类问题的有效手段之一。
Semi-supervised Zero-Shot Learning by a Clustering-based Approach In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can re...
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...
Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagate known labels through the edges of the...
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)10 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...