Year: 2021
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institutions: Nvidia (United Kingdom), Nvidia (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3201854397