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The Inclusive Images Competition

Conference paper
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Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

Popular large image classification datasets that are drawn from the web present Eurocentric and Americentric biases that negatively impact the generalizability of models trained on them Shreya Shankar et al. (No classification without representation: Assessing geodiversity issues in open data sets for the developing world. arXiv preprint arXiv:1711.08536, 2017). In order to encourage the development of modeling approaches that generalize well to images drawn from locations and cultural contexts that are unseen or poorly represented at the time of training, we organized the Inclusive Images competition in association with Kaggle and the NeurIPS 2018 Competition Track Workshop. In this chapter, we describe the motivation and design of the competition, present reports from the top three competitors, and provide high-level takeaways from the competition results.

Notes

Acknowledgements

We would like to thank the organizers of the NeurIPS 2018 Competition Track workshop and participants in the campaign for Inclusive Images. WorldWideInclusive would like to thank David Austin and Anil Thomas for participating as team members in this challenge.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Google ResearchCambridgeUSA
  2. 2.Samsung AI CenterMoscowRussia
  3. 3.Steklov Institute of Mathematics at St. PetersburgSaint PetersburgRussia
  4. 4.Neuromation OUTallinnEstonia
  5. 5.DniproUkraine
  6. 6.Institute for Design Problems in Microelectronics of Russian Academy of Sciences (IPPM RAS)MoscowRussia
  7. 7.Alexa Machine LearningAmazonCambridgeUSA
  8. 8.Computational Science LaboratoryUniversitat Pompeu Fabra (PRBB)BarcelonaSpain

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