A Data-Centric Approach for Training Deep Neural Networks with Less Data

Understand

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.