In this paper, we explore this novel task, which attempts to learn a generator directly from a pre-trained classifier, without the assistance of any training data.

In this work we study the problem of actively learning binary classifiers from a given concept class, i.e., learning by utilizing unlabeled data and submitting targeted queries about their labels to a domain expert. We evaluate the quality of our solutions by considering the learning curves they induce, i.e., the rate of

Recently large language models like GPT -3 have shown encouraging performance as implicit knowledge source and revealed planning abilities.