A Data-Centric Approach for Training Deep Neural Networks with Less Data
  While the availability of large datasets is perceived to be a key requirement
for training deep neural networks, it is possible to train such models with
relatively little data. However, compensating for the absence of large datasets
demands a series of actions to enhance the quality of the existing samples and
to generate new ones. This paper summarizes our winning submission to the
"Data-Centric AI" competition. We discuss some of the challenges that arise
while training with a small dataset, offer a principled approach for systematic
data quality enhancement, and propose a GAN-based solution for synthesizing new
data points. Our evaluations indicate that the dataset generated by the
proposed pipeline offers 5% accuracy improvement while being significantly
smaller than the baseline.
