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📄 Abstract
Abstract: The number of studies that combine Evolutionary Machine Learning and
self-supervised learning has been growing steadily in recent years.
Evolutionary Machine Learning has been shown to help automate the design of
machine learning algorithms and to lead to more reliable solutions.
Self-supervised learning, on the other hand, has produced good results in
learning useful features when labelled data is limited. This suggests that the
combination of these two areas can help both in shaping evolutionary processes
and in automating the design of deep neural networks, while also reducing the
need for labelled data. Still, there are no detailed reviews that explain how
Evolutionary Machine Learning and self-supervised learning can be used
together. To help with this, we provide an overview of studies that bring these
areas together. Based on this growing interest and the range of existing works,
we suggest a new sub-area of research, which we call Evolutionary
Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out
some of the main challenges and suggest directions for future research to help
Evolutionary Self-Supervised Learning grow and mature as a field.
Key Contributions
This survey provides a comprehensive overview of the intersection between Evolutionary Machine Learning (EML) and Self-Supervised Learning (SSL). It highlights how EML can automate ML algorithm design and how SSL reduces the need for labeled data, suggesting their combination, termed Evolutionary Self-Supervised Learning, as a promising new research area.
Business Value
Accelerates the development of more effective and efficient AI models by automating parts of the design process and reducing the cost associated with data labeling. This can lead to faster innovation cycles in AI product development.