Beyond Supervised Learning: A Computer Vision Perspective

Abstract

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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Chum, L., Subramanian, A., Balasubramanian, V.N. et al. Beyond Supervised Learning: A Computer Vision Perspective. J Indian Inst Sci 99, 177–199 (2019). https://doi.org/10.1007/s41745-019-0099-3

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Keywords

  • Deep learning
  • Synthetic data
  • Domain adaptation
  • Weakly supervised learning
  • Few-shot learning
  • Self-supervised learning