Mental bootstrapping enables human-level concept learning in self-supervised deep models
A self-supervised framework that trains models on systematically simplified versions of abstract reasoning tasks containing incomplete but structured concept cues enables models to form internal abstractions under limited resources and later apply them to more complex problems.