  Supervised machine learning based state-of-the-art computer vision techniques
are in general data hungry. Their data curation poses the challenges of
expensive human labeling, inadequate computing resources and larger experiment
turn around times. Training data subset selection and active learning
techniques have been proposed as possible solutions to these challenges. A
special class of subset selection functions naturally model notions of
diversity, coverage and representation and can be used to eliminate redundancy
thus lending 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 Dispersion models for training-data subset selection and
reducing labeling effort. We demonstrate this across the board for a variety of
computer vision tasks including Gender Recognition, Face Recognition, Scene
Recognition, Object Detection and Object Recognition. Our results show that
diversity based subset selection done in the right way can increase the
accuracy by upto 5 - 10% over existing baselines, particularly in settings in
which less training data is available. This allows the training of complex
machine learning models like Convolutional Neural Networks with much less
training data and labeling costs while incurring minimal performance loss.
