  Labeling data is one of the most costly processes in machine learning
pipelines. Active learning is a standard approach to alleviating this problem.
Pool-based active learning first builds a pool of unlabelled data and
iteratively selects data to be labeled so that the total number of required
labels is minimized, keeping the model performance high. Many effective
criteria for choosing data from the pool have been proposed in the literature.
However, how to build the pool is less explored. Specifically, most of the
methods assume that a task-specific pool is given for free. In this paper, we
advocate that such a task-specific pool is not always available and propose the
use of a myriad of unlabelled data on the Web for the pool for which active
learning is applied. As the pool is extremely large, it is likely that relevant
data exist in the pool for many tasks, and we do not need to explicitly design
and build the pool for each task. The challenge is that we cannot compute the
acquisition scores of all data exhaustively due to the size of the pool. We
propose an efficient method, Seafaring, to retrieve informative data in terms
of active learning from the Web using a user-side information retrieval
algorithm. In the experiments, we use the online Flickr environment as the pool
for active learning. This pool contains more than ten billion images and is
several orders of magnitude larger than the existing pools in the literature
for active learning. We confirm that our method performs better than existing
approaches of using a small unlabelled pool.
