Label propagation is a semi-supervised technique that makes use of the labeled and unlabeled data to learn about the unlabeled data. Quite often, data that will benefit from a classification algorithm is difficult to label. For example, labeling data might be very expensive, so only a subset i...
Due to the considerable time and expense required in labeling data, a challenge is to propose learning algorithms that can learn from a small amount of labeled data and a much larger amount of unlabeled data. In this paper, we propose one such algorithm which uses an evolutionary strategy to...
LabelPropagation出自论文“Learning from Labeled and Unlabeled Data with Label Propagation”,目前已在gtrick中实现: gtrick/label_prop.py at main · sangyx/gtrick (github.com)github.com/sangyx/gtrick/blob/main/gtrick/dgl/label_prop.py 关于gtrick的介绍: gtrick: GNN的trick工具箱143 赞同 ·...
本文从理论和实践两名面重新研究了监督Label的价值,提出了应用未标注数据(unlabeled data)进行半监督学习(semi-supervised)以及无需额外数据,进行自监督学习(self-supervised)预训练,其训练结果参数作为常规监督学习网络初始化参数,在large-scale imbalanced benchmarks达到SOTA。 论文地址:paper 代码地址:code 从定性...
But machine learning needs fuel to work on, and this fuel is labeled data. We dedicated the last two articles to understanding labeled and unlabeled data, why and how to use both types. Now, let's see how the data is annotated and what you should do before the labeling starts. Here ...
We propose the simple and efficient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For un-labeled data, Pseudo-Label s, just picking up the class which has the maximum...
The Label Propagation algorithm is a graph-based semi-supervised learning method. Its basic principle is to predict the label information about unlabeled nodes using that
Paper tables with annotated results for Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation
To achieve this goal, we propose a hierarchical label propagation technique based on quasi-flat zones theory, that learns from labeled and unlabeled data and propagates names through a multimodal graph representation. In this representation, we combine audio, video, and text processing techniques to...
co-training方法:1998Combining labeled and unlabeled data with co-training 两个DNN同时训练:2017Decoupling" when to update" from" how to update" Co-teaching Method 主要思路是同时训练两个DNN,即每个mini-batch的samples先通过单个DNN,然后选出具有较小loss的sample作为clean label的samples,然后将选出来的clea...