  While machine learning approaches to visual recognition offer great promise,
most of the existing methods rely heavily on the availability of large
quantities of labeled training data. However, in the vast majority of
real-world settings, manually collecting such large labeled datasets is
infeasible due to the cost of labeling data or the paucity of data in a given
domain. In this paper, we present a novel Adversarial Knowledge Transfer (AKT)
framework for transferring knowledge from internet-scale unlabeled data to
improve the performance of a classifier on a given visual recognition task. The
proposed adversarial learning framework aligns the feature space of the
unlabeled source data with the labeled target data such that the target
classifier can be used to predict pseudo labels on the source data. An
important novel aspect of our method is that the unlabeled source data can be
of different classes from those of the labeled target data, and there is no
need to define a separate pretext task, unlike some existing approaches.
Extensive experiments well demonstrate that models learned using our approach
hold a lot of promise across a variety of visual recognition tasks on multiple
standard datasets.
