Beyond Supervised Learning: A Computer Vision Perspective

Chum, Lovish and Anbumani, Subramanian and Balasubramanian, Vineeth N and C V, Jawahar (2019) Beyond Supervised Learning: A Computer Vision Perspective. Journal of the Indian Institute of Science. ISSN 0970-4140

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Fully supervised deep learning-based methods have created a profound impact in various fields of computer science. Compared to classical methods, supervised deep learning-based techniques face scalability issues as they require huge amounts of labeled data and, more significantly, are unable to generalize to multiple domains and tasks. In recent years, a lot of research has been targeted towards addressing these issues within the deep learning community. Although there have been extensive surveys on learning paradigms such as semi-supervised and unsupervised learning, there are a few timely reviews after the emergence of deep learning. In this paper, we provide an overview of the contemporary literature surrounding alternatives to fully supervised learning in the deep learning context. First, we summarize the relevant techniques that fall between the paradigm of supervised and unsupervised learning. Second, we take autonomous navigation as a running example to explain and compare different models. Finally, we highlight some shortcomings of current methods and suggest future directions.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Deep learning, Synthetic data, Domain adaptation, Weakly supervised learning, Few-shot learning, Self-supervised learning
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 04 Mar 2019 03:59
Last Modified: 04 Mar 2019 03:59
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