  State-of-the-art techniques of artificial intelligence, in particular deep
learning, are mostly data-driven. However, collecting and manually labeling a
large scale dataset is both difficult and expensive. A promising alternative is
to introduce synthesized training data, so that the dataset size can be
significantly enlarged with little human labor. But, this raises an important
problem in active vision: given an {\bf infinite} data space, how to
effectively sample a {\bf finite} subset to train a visual classifier? This
paper presents an approach for learning from synthesized data effectively. The
motivation is straightforward -- increasing the probability of seeing difficult
training data. We introduce a module named {\bf SampleAhead} to formulate the
learning process into an online communication between a {\em classifier} and a
{\em sampler}, and update them iteratively. In each round, we adjust the
sampling distribution according to the classification results, and train the
classifier using the data sampled from the updated distribution. Experiments
are performed by introducing synthesized images rendered from ShapeNet models
to assist PASCAL3D+ classification. Our approach enjoys higher classification
accuracy, especially in the scenario of a limited number of training samples.
This demonstrates its efficiency in exploring the infinite data space.
