  We introduce an interactive learning framework for the development and
testing of intelligent visual systems, called learning-by-asking (LBA). We
explore LBA in context of the Visual Question Answering (VQA) task. LBA differs
from standard VQA training in that most questions are not observed during
training time, and the learner must ask questions it wants answers to. Thus,
LBA more closely mimics natural learning and has the potential to be more
data-efficient than the traditional VQA setting. We present a model that
performs LBA on the CLEVR dataset, and show that it automatically discovers an
easy-to-hard curriculum when learning interactively from an oracle. Our LBA
generated data consistently matches or outperforms the CLEVR train data and is
more sample efficient. We also show that our model asks questions that
generalize to state-of-the-art VQA models and to novel test time distributions.
