即将模型的决策决策边界推离不同类别的类别边界(这在interpolation consistency training for semi-supervised learning 里提到了,感觉还是比较符合直观感受的),这样当输入发生轻微扰动的时候不容易发生严重的跳变而是label的预测结果发生较为平缓的变动
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has ...
importtorchfromsemilearn.core.algorithmbaseimportAlgorithmBasefromsemilearn.core.utilsimportALGORITHMSfromsemilearn.algorithms.hooksimportPseudoLabelingHook,FixedThresholdingHookfromsemilearn.algorithms.utilsimportSSL_Argument,str2bool@ALGORITHMS.register('fixmatch')classFixMatch(AlgorithmBase):"""FixMatch algorithm (...
和Deep带来的好处! Lecture 9:Semi-supervised 1.Introduction (1)半监督学习的训练数据,有一部分是标注数据,有一部分是无标注数据。 (2)Transductive learning和Inductive learning都可算是半监督学习,区别在于前者的无标注数据是测试数据(除去label),而后者的无标注数据不包括测试数据。实际中用哪种常取决于是否有测...
Semi-supervised Learning - Deep Learning Dictionary The two categories of supervised learning and unsupervised learning are divided based on whether or not the training data has corresponding labels that are supplied to the learning algorithm during training. Semi-supervised learning fits right in the...
Semi-supervised Deep Learning for Fully Convolutional Networks文章解读,程序员大本营,技术文章内容聚合第一站。
SSLSemi-Supervised Learning SUPSupervised Learning _uUnsupervised UBS8KUrbanSound8K dataset References [1] K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Ku- rakin, H. Zhang, and C. Raffel, “FixMatch: Simplifying Semi-Supervised Learning with Consistency and...
Pseudo-Label:The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks,程序员大本营,技术文章内容聚合第一站。
论文链接: https://cs.nju.edu.cn/liyf/paper/icml20-DS3L.pdf代码链接: https://github.com/guolz-ml/DS3L周志华课题组在ICML 2020上投稿的工作。 Abstract当半监督学习的类别分布不平衡时,算法的性能会严重下…
深度半监督学习(Deep semi-supervised learning,简写Deep SSL)已经证明了是同时利用未标记数据和标记数据进行训练的一种有效方法。但目前仍存在诸多问题,一个很重要的问题是,当未标记数据和标记数据不在同一分布下。一个简单例子是,未标记数据包含一些标记数据中没有看到的类,模型的性能会严重下降甚至比仅利用标记数据...