Semi-Supervised Challenge

Overview

This challenge is focused on learning from partially labeled data, a form of semi-supervised learning. This dataset is designed to expose some of the challenges encountered in a realistic setting, such as the fine-grained similarity between classes, significant class imbalance, and domain mismatch between the labeled and unlabeled data. This challenge is part of the FGVC^7 workshop at CVPR 2020. Teams with top submissions, at the discretion of the workshop organizers, will be invited to present their work at the workshop. The competition will be hosted on Kaggle.

Competition Details

The competition is hosted on Kaggle here with more information available on our github page.

Competition Begins March 9 2020

Submission Deadline May 11 2020

Organizers

Jong-Chyi Su (UMass Amherst)

Acknowledgements

FGVC team, Kaggle team, iNaturalist, and special thanks to Grant van Horn for help with iNaturalist data