Li Yufeng,Kwok J,Zhou Zhihua.Semi-supervised learning using label mean[C]??Proc of the 26th Int Conf on Machine Learning.New York:ACM,2009:633-640Li Yufeng,Kwok J,Zhou Zhihua.Semi-supervised learningusing label mean. Proc of the 26th Int Conf on MachineLearning . 2009...
深度学习 (deep learning) 通过监督学习 (supervised learning) 在大量的机器学习任务上取得了瞩目的成就, 如 ImageNet 上超过 90% 的分类准确率, Cityscapes 上超过 85% 的分割准确率. 然而, 实现高精度的分类, 分割等任务需要大规模有标签的训练数据...
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results 论文链接: https://arxiv.org/abs/1703.01780 代码链接: https://github.com/CuriousAI/mean-teacher 如上图所示,Mean Teachers 则是 Temporal Ensembling 的改进版,Temporal Ensembling 对模型...
Inductive methods, like supervised learning methods, yield a classification model that can be used to predict the label of previously unseen data points.Transductive methodsdo not yield such a model, but instead directly provide predictions. Inductive methodsinvolve optimization over prediction models, wh...
可以看出在label较少的时候,mean teacher要好很多,部分情况mean teacher不是最佳。值得注意的是原始Π-model在label不全的时候比Π (ours)好,这似乎无法说明在Π-model的情况下,令模型使用weight-averaged consistency会得到更好的效果。 先看到这里,其他实验部分就不赘述了。如有不通意见,欢迎评论区交流。
semi-supervised learning半监督学习 Semi-supervisedLearning2017.11 65 1 Traditionalmachinelearningparadigms SupervisedlearningLearningfromlabeleddata,,e.g.,classification,regression Unsupervisedlearning Learningfromdatawithoutlabel，e.g.,clustering data 65 2 Isdataenough?•Bigdataera,obtainingdataisgetting...
Challengesof using semi-supervised learning As we already mentioned, one of the significant benefits of applying semi-supervised learning is that it has high model performance without being too expensive to prepare data. It doesn’t mean, of course, that SSL has no limitations. Let’s discuss ...
The cluster-then-label algorithm using deep learning plus k-means architecture can significantly improve the classifiers’ performance on data from out-of-distribution subjects, but we observe some random failures of the algorithm during the experiments. This indicates that a successful execution of the...
proposed using unlabeled data to train a large task-agnostic unsupervised model, fine-tuning it with label-supervision, then returning to unlabeled data to perform self-training on a task-specific model. Learn more about self-supervised learning in The Beginner's Guide to Self-Supervised Learning,...
What is Semi-Supervised Learning? It is a special form of classification. Traditional classifiers use only labeled data (feature / label pairs) to train. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotator...