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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.
The application of inference-time prompt-embedding optimization for text-to-image generation using Stable Diffusion XL Turbo indicates that evolutionary optimization is a robust method for exploring embedding spaces without the need for gradients or model fine-tuning.
A new sub-area of research is suggested, which is called Evolutionary Self-Supervised Learning, which is suggested to help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data.
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