Low-budget label query through domain alignment enforcement

Deep learning revolution happened thanks to the availability of a massive amount of labeled data which contributed to the development of models with extraordinary inference capabilities. Despite the public availability of large-scale datasets, to address specific requirements it is often necessary to generate a new set of labeled data whose production is often costly and require specific know-how to be fulfilled. In this work, we propose the new problem of , which aims at maximizing the classification performance by selecting a convenient and small set of samples (