Selvsuperviseret
Selvsuperviseret is the Danish term for self-supervised learning, a machine learning paradigm in which models learn useful representations from unlabeled data by constructing and solving automatic auxiliary tasks. The supervisory signal is generated from the data itself, removing or reducing the need for manually labeled examples. This approach aims to learn rich features that transfer to downstream tasks with limited labeled data.
In practice, models are trained on pretext tasks such as predicting missing parts of an input, colorizing
After learning from the unlabeled data, the resulting representations can be used for downstream supervised tasks,
Advantages include reduced annotation requirements, better generalization through large-scale pretraining, and improved transferability across tasks. Challenges