Deep representation learning: Fundamentals, Perspectives, Applications,
and Open Challenges
- URL: http://arxiv.org/abs/2211.14732v1
- Date: Sun, 27 Nov 2022 05:37:00 GMT
- Title: Deep representation learning: Fundamentals, Perspectives, Applications,
and Open Challenges
- Authors: Kourosh T. Baghaei, Amirreza Payandeh, Pooya Fayyazsanavi, Shahram
Rahimi, Zhiqian Chen, Somayeh Bakhtiari Ramezani
- Abstract summary: We discuss the principles and developments that have been made in the process of learning representations.
For each framework or model, the key issues and open challenges, as well as the advantages, are examined.
- Score: 3.9675935847246677
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine Learning algorithms have had a profound impact on the field of
computer science over the past few decades. These algorithms performance is
greatly influenced by the representations that are derived from the data in the
learning process. The representations learned in a successful learning process
should be concise, discrete, meaningful, and able to be applied across a
variety of tasks. A recent effort has been directed toward developing Deep
Learning models, which have proven to be particularly effective at capturing
high-dimensional, non-linear, and multi-modal characteristics. In this work, we
discuss the principles and developments that have been made in the process of
learning representations, and converting them into desirable applications. In
addition, for each framework or model, the key issues and open challenges, as
well as the advantages, are examined.
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