Some companies, such as Drive, are using deep learning to enhance automation for annotating data, as a way to accelerate the tedious process of data labelling. Let’s use unlabeled data Koopman, however, believes there is another way to “squeeze the value out of the accumulated data.” How...
However, in many real world applications, the acquisition of sufficient amounts of labeled training data is costly, while unlabeled data is usually easily to be obtained. In this paper, we study the problem of learning discriminative features (segments) from both labeled and unlabeled time series...
Combining_labeled_and_unlabeled_data_with_co-training 下载积分: 3000 内容提示: Combining Lab eled and Unlab eled Data with Co-Training? yAvrim BlumScho ol of Computer ScienceCarnegie Mellon UniversityPittsburgh, PA 15213-3891avrim+@cs.cmu.eduTom MitchellScho ol of Computer ScienceCarnegie Mellon...
Combining labeled and unlabeled data with co-training:(与co-training结合标记和未标记数据).pdf,Combining Lab eled and Unlab eled Data with CoTraining y Avrim Blum Tom Mitchell School of Computer Science School of Computer Science Carnegie Mellon Univer
Zhu X. and Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, 2002. 概 本文通过将有标签数据传播给无标签数据
Unlabeled dataIn real-world data mining applications, it is often the case that unlabeled instances are abundant, while available labeled instances are very limited. Thus, semi-supervised learning, which attempts to benefit from large amount of unlabeled data together with labeled data, has attracted...
Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang 2021 International Conference on Machine Learning|July 2021 In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supe...
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task lear...
labeled data can help us learn the distribution over ob- ject descriptions. Links among the unlabeled data (or test set) can pro- vide information that can help with classification. Links between the labeled training data and unlabeled ...
Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. ...