The human choices that impact AI – A survey on design choices for self-supervised learning in computer vision
Ladyna Wittscher
Behind every self-supervised vision model lies a chain of human design choices that shape its performance, robustness, and transferability. Choices regarding pretext data, pretext tasks, model architecture, and transfer strategies matter. Successful self-supervision depends on their alignment.