While supervised (and semi-supervised) learning requires an external “ground truth,” in the form of labeled data, self-supervised learning tasks derive the ground truth from the underlying structure of unlabeled samples. Many self-supervised tasks are not useful unto themselves: their utility lies...
In our recent work, “Unsupervised Data Augmentation (UDA) for Consistency Training”, we demonstrate that one can also perform data augmentation on unlabeled data to significantly improvesemi-supervised learning(SSL). Our results support the recentrevival of semi-supervised learning, showing that: (1...
Figure 1: Diagram of the label guessing process used in MixMatch. Stochastic data augmentation is applied to an unlabeled image K times, and each augmented image is fed through the classifier. Then, the average of these K predictions is “sharpened” by adjusting the distribution’s temperature....
Recently, machine learning algorithms have been considered effective for bearing diagnosis. However, most of these methods are supervised learning and thus suffer from data imbalance in the practical applications. This study proposes a semi-supervised learning indicator called outlier rate diagram (ORgram...
[29].However, semi-supervised learning approaches focus on exploiting the unlabeled data that belong to the same domain as labeled ones, leaving rich cross-modality data unexploited.Note:这句觉得不太好,SSL的原始定义就是相同分布,部分有标注,这是其特点;不过在存在大量异源数据可以使用的情况下,将其称...
Recently, machine learning algorithms have been considered effective for bearing diagnosis. However, most of these methods are supervised learning and thus suffer from data imbalance in the practical applications. This study proposes a semi-supervised learning indicator called outlier rate diagram (ORgram...
Figure 3 shows a flow diagram of the semi-supervised incremental learning approach. Fig. 3 Flow diagram of incremental learning. Rounded rectangles show the beginning and the end of the iterations, rectangles are the rule sets, the broken line rectangle represents the seed set performance, ovals ...
a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vect...
a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vect...
A schematic diagram of the whole training process is presented in Figure 3 and we describe the losses used in VideoSSL in the following sections. 3.1. Learning from Labeled Data Let X = {x1, . . . , xK } denote the annotated video clips with corresponding category i...