VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

Understand

We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges.

• We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set.
• Furthermore, to encourage data efficient solutions, we prohibited the use of pre-trained models and other transfer learning techniques.
• The majority of top ranking solutions make heavy use of data augmentation, model ensembling, and novel and efficient network architectures to achieve significant performance increases compared to the provided baselines.