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

By Mohammad Motamedi, Nikolay Sakharnykh, Tim Kaldewey. 2021

72 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Douwe Kiela
  2. Mohit Bansal
  3. Nazneen Rajani
  4. Tristan Thrush
  5. Yacine Jernite
  6. Yejin Choi
  7. Margaret Mitchell
  8. Angelina McMillan-Major

In Computer Vision and Pattern Recognition

  1. Douwe Kiela
  2. Nazneen Rajani
  3. Tristan Thrush
  4. Angelina McMillan-Major
  5. Yacine Jernite
  6. Alexandra Sasha Luccioni
  7. Margaret Mitchell
  8. Mohit Bansal

In Evaluation & Benchmarks

  1. Tristan Thrush
  2. Douwe Kiela
  3. Angelina McMillan-Major
  4. Yacine Jernite
  5. Alexandra Sasha Luccioni
  6. Margaret Mitchell
  7. Nazneen Rajani
  8. Mohit Bansal

Citations in the corpus