Evolutionary Machine Learning Meets Self-Supervised Learning: A Comprehensive Survey
TL;DR
This work introduces Evolutionary Self-Supervised Learning as a research area and proposes a taxonomy based on two core directions, evolution applied to Self-Supervised learning and Self-Supervised learning applied to evolution.
Abstract
Research that combines Evolutionary Machine Learning and Self-Supervised Learning has been steadily increasing in recent years, suggesting 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. Yet, no survey details how these two areas are used together. To help with this, we introduce Evolutionary Self-Supervised Learning as a research area and propose a taxonomy based on two core directions, evolution applied to Self-Supervised learning and Self-Supervised learning applied to evolution. For each direction, we categorise and discuss different approaches, compare the performance of different works, and discuss observed trends and their impact on performance and computational cost. Following this, we identify six open challenges around pretext task design, ablation studies with label scarcity, and the design of reliable and low-cost fitness metrics that take into account self-supervised mechanics. Based on these challenges, we propose research directions for researchers within the field.