CLS: Cross Labeling Supervision for Semi-Supervised Learning

• URL: http://arxiv.org/abs/2202.08502v1
• Date: Thu, 17 Feb 2022 08:09:40 GMT
• Title: CLS: Cross Labeling Supervision for Semi-Supervised Learning
• Authors: Yao Yao, Junyi Shen, Jin Xu, Bin Zhong, Li Xiao
• Abstract summary: Cross Labeling Supervision ( CLS) is a framework that generalizes the typical pseudo-labeling process. CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.
• Score: 9.929229055862491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is well known that the success of deep neural networks is greatly attributed to large-scale labeled datasets. However, it can be extremely time-consuming and laborious to collect sufficient high-quality labeled data in most practical applications. Semi-supervised learning (SSL) provides an effective solution to reduce the cost of labeling by simultaneously leveraging both labeled and unlabeled data. In this work, we present Cross Labeling Supervision (CLS), a framework that generalizes the typical pseudo-labeling process. Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning. To mitigate the confirmation bias of self-labeling and boost the tolerance to false labels, two different initialized networks with the same structure are trained simultaneously. Each network utilizes high-confidence labels from the other network as additional supervision signals. During the label generation phase, adaptive sample weights are assigned to artificial labels according to their prediction confidence. The sample weight plays two roles: quantify the generated labels' quality and reduce the disruption of inaccurate labels on network training. Experimental results on the semi-supervised classification task show that our framework outperforms existing approaches by large margins on the CIFAR-10 and CIFAR-100 datasets.

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