2021
DOI: 10.48550/arxiv.2110.13740
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples

Abstract: The scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning (SSL) provides a promising way to leverage unlabeled data by pseudo labels. However, when the size of labeled data is very small (say a few labeled samples per class), SSL performs poorly and unstably, possibly due to the low quality of learned pseudo labels. In this paper, we propose a new SSL method called DP-SSL that adopts an innovative data programming (DP) scheme to generate probabilistic labels for unlabeled d… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 30 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?