  Which samples should be labelled in a large data set is one of the most
important problems for trainingof deep learning. So far, a variety of active
sample selection strategies related to deep learning havebeen proposed in many
literatures. We defined them as Active Deep Learning (ADL) only if
theirpredictor is deep model, where the basic learner is called as predictor
and the labeling schemes iscalled selector. In this survey, three fundamental
factors in selector designation were summarized. Wecategory ADL into
model-driven ADL and data-driven ADL, by whether its selector is model-drivenor
data-driven. The different characteristics of the two major type of ADL were
addressed in indetail respectively. Furthermore, different sub-classes of
data-driven and model-driven ADL are alsosummarized and discussed emphatically.
The advantages and disadvantages between data-driven ADLand model-driven ADL
are thoroughly analyzed. We pointed out that, with the development of
deeplearning, the selector in ADL also is experiencing the stage from
model-driven to data-driven. Finally,we make discussion on ADL about its
uncertainty, explanatory, foundations of cognitive science etc.and survey on
the trend of ADL from model-driven to data-driven.
