In semi-supervised learning (aka weak supervision), a small amount of labeled data is used along with a large amount of unlabeled data to train a deep model. In other words, the desired output values are provided only for a subset of the training data. The remaining data is unlabeled or imprecisely labeled.
Intuitively, it can be seen as an exam and labeled data as sample problems that the teacher solves for the class as an aid in solving another set of problems. In the transductive setting, these unsolved problems act as exam questions. In the inductive setting, they become practice problems of the sort that will make up the exam.