An Overview of Deep Semi-Supervised Learning
- URL: http://arxiv.org/abs/2006.05278v2
- Date: Mon, 6 Jul 2020 17:38:19 GMT
- Title: An Overview of Deep Semi-Supervised Learning
- Authors: Yassine Ouali, C\'eline Hudelot, Myriam Tami
- Abstract summary: There is a rising research interest in semi-supervised learning and its applications to deep neural networks.
This paper provides a comprehensive overview of deep semi-supervised learning, starting with an introduction to the field.
- Score: 8.894935073145252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks demonstrated their ability to provide remarkable
performances on a wide range of supervised learning tasks (e.g., image
classification) when trained on extensive collections of labeled data (e.g.,
ImageNet). However, creating such large datasets requires a considerable amount
of resources, time, and effort. Such resources may not be available in many
practical cases, limiting the adoption and the application of many deep
learning methods. In a search for more data-efficient deep learning methods to
overcome the need for large annotated datasets, there is a rising research
interest in semi-supervised learning and its applications to deep neural
networks to reduce the amount of labeled data required, by either developing
novel methods or adopting existing semi-supervised learning frameworks for a
deep learning setting. In this paper, we provide a comprehensive overview of
deep semi-supervised learning, starting with an introduction to the field,
followed by a summarization of the dominant semi-supervised approaches in deep
learning.
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