  Supervised machine learning based state-of-the-art computer vision techniques
are in general data hungry and pose the challenges of not having adequate
computing resources and of high costs involved in human labeling efforts.
Training data subset selection and active learning techniques have been
proposed as possible solutions to these challenges respectively. A special
class of subset selection functions naturally model notions of diversity,
coverage and representation and they can be used to eliminate redundancy and
thus lend themselves well for training data subset selection. They can also
help improve the efficiency of active learning in further reducing human
labeling efforts by selecting a subset of the examples obtained using the
conventional uncertainty sampling based techniques. In this work we empirically
demonstrate the effectiveness of two diversity models, namely the
Facility-Location and Disparity-Min models for training-data subset selection
and reducing labeling effort. We do this for a variety of computer vision tasks
including Gender Recognition, Scene Recognition and Object Recognition. Our
results show that subset selection done in the right way can add 2-3% in
accuracy on existing baselines, particularly in the case of less training data.
This allows the training of complex machine learning models (like Convolutional
Neural Networks) with much less training data while incurring minimal
performance loss.
